Using External Input Data in Off-the-shelf Web Scrapers

There is a question I’ve wanted to shed some light upon for a long time already: “What if I need to scrape several URL’s based on data in some external database?“.

For example, recently one of our visitors asked a very good question (thanks, Ed):

    “I have a large list of amazon.com asin. I would like to scrape 10 or so fields for each asin. Is there any web scraping software available that can read each asin from a database and form the destination url to be scraped like http://www.amazon.com/gp/product/{asin} and scrape the data?”

This question impelled me to investigate this matter. I contacted several web scraper developers, and they kindly provided me with detailed answers that allowed me to bring the following summary to your attention:
Visual Web Ripper

An input data source can be used to provide a list of input values to a data extraction project. A data extraction project will be run once for each row of input values. You can find the additional information here.
Web Content Extractor

You can use the -at”filename” command line option to add new URLs from TXT or CSV file:

    WCExtractor.exe projectfile -at”filename” -s

projectfile: the file name of the project (*.wcepr) to open.
filename – the file name of the CSV or TXT file that contains URLs separated by newlines.
-s – starts the extraction process

You can find some options and examples here.
Mozenda

Since Mozenda is cloud-based, the external data needs to be loaded up into the user’s Mozenda account. That data can then be easily used as part of the data extracting process. You can construct URLs, search for strings that match your inputs, or carry through several data fields from an input collection and add data to it as part of your output. The easiest way to get input data from an external source is to use the API to populate data into a Mozenda collection (in the user’s account). You can also input data in the Mozenda web console by importing a .csv file or importing one through our agent building tool.

Once the data is loaded into the cloud, you simply initiate building a Mozenda web agent and refer to that Data list. By using the Load page action and the variable from the inputs, you can construct a URL like http://www.amazon.com/gp/product/%asin%.
Helium Scraper

Here is a video showing how to do this with Helium Scraper:


The video shows how to use the input data as URLs and as search terms. There are many other ways you could use this data, way too many to fit in a video. Also, if you know SQL, you could run a query to get the data directly from an external MS Access database like
SELECT * FROM [MyTable] IN "C:\MyDatabase.mdb"

Note that the database needs to be a “.mdb” file.
WebSundew Data Extractor
Basically this allows using input data from external data sources. This may be CSV, Excel file or a Database (MySQL, MSSQL, etc). Here you can see how to do this in the case of an external file, but you can do it with a database in a similar way (you just need to write an SQL script that returns the necessary data).
In addition to passing URLs from the external sources you can pass other input parameters as well (input fields, for example).
Screen Scraper

Screen Scraper is really designed to be interoperable with all sorts of databases. We have composed a separate article where you can find a tutorial and a sample project about scraping Amazon products based on a list of their ASINs.


Source: http://extract-web-data.com/using-external-input-data-in-off-the-shelf-web-scrapers/

Web Data Extraction Services

Web Data Extraction from Dynamic Pages includes some of the services that may be acquired through outsourcing. It is possible to siphon information from proven websites through the use of Data Scrapping software. The information is applicable in many areas in business. It is possible to get such solutions as data collection, screen scrapping, email extractor and Web Data Mining services among others from companies providing websites such as Scrappingexpert.com.

Data mining is common as far as outsourcing business is concerned. Many companies are outsource data mining services and companies dealing with these services can earn a lot of money, especially in the growing business regarding outsourcing and general internet business. With web data extraction, you will pull data in a structured organized format. The source of the information will even be from an unstructured or semi-structured source.

In addition, it is possible to pull data which has originally been presented in a variety of formats including PDF, HTML, and test among others. The web data extraction service therefore, provides a diversity regarding the source of information. Large scale organizations have used data extraction services where they get large amounts of data on a daily basis. It is possible for you to get high accuracy of information in an efficient manner and it is also affordable.

Web data extraction services are important when it comes to collection of data and web-based information on the internet. Data collection services are very important as far as consumer research is concerned. Research is turning out to be a very vital thing among companies today. There is need for companies to adopt various strategies that will lead to fast means of data extraction, efficient extraction of data, as well as use of organized formats and flexibility.

In addition, people will prefer software that provides flexibility as far as application is concerned. In addition, there is software that can be customized according to the needs of customers, and these will play an important role in fulfilling diverse customer needs. Companies selling the particular software therefore, need to provide such features that provide excellent customer experience.

It is possible for companies to extract emails and other communications from certain sources as far as they are valid email messages. This will be done without incurring any duplicates. You will extract emails and messages from a variety of formats for the web pages, including HTML files, text files and other formats. It is possible to carry these services in a fast reliable and in an optimal output and hence, the software providing such capability is in high demand. It can help businesses and companies quickly search contacts for the people to be sent email messages.

It is also possible to use software to sort large amount of data and extract information, in an activity termed as data mining. This way, the company will realize reduced costs and saving of time and increasing return on investment. In this practice, the company will carry out Meta data extraction, scanning data, and others as well.

please visit Data extraction services to take care of your online as well as offline projects and to get your work done in given time frame with exceptional quality.




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Data Conversion Services

Data conversion services have a unique place in this internet driven, fast-growing business world. Whatever be the field - educational, health, legal, research or any other - data conversion services play a crucial role in building and maintaining the records, directories and databases of a system. With this service, firms can convert their files and databases from one format or media to another.

Data conversion services help firms to convert their valuable data and information stored and accumulated in papers into digital format for long-term storage - for the purpose of archiving, easy searching, accessing and sharing.

Now there are many big and small highly competent business process outsourcing (BPO) companies providing a full range of reliable and trustworthy data conversion services to the clients worldwide. Most of these BPO firms are fully equipped with excellent infrastructural facilities and skilled manpower to provide data conversion services catering to the clients' expectations and specifications. These firms can effectively play an important role in improving a company's document/data lifecycle management. With the application of high speed scanners and data processors, these firms can expertly and accurately convert any voluminous and complex data into digital formats, all within the specified time and budget. Moreover, they use state-of-the-art encryption techniques to ensure privacy and security of data transmission over the Internet. The following are the important services offered by the companies in this area:

o Document scanning and conversion
o File format conversion
o XML conversion
o SGML conversion
o CAD conversion
o OCR clean up, ICR, OMR
o Image Conversion
o Book conversion
o HTML conversion
o PDF conversion
o Extracting data from catalog
o Catalog conversion
o Indexing
o Scanning from hard copies, microfilms, microfiche, aperture cards, and large-scale drawings

Thus, by entrusting a data conversion project to an expert outsourcing company, firms can enjoy numerous advantages in terms of quality, efficiency and cost. Some of its key benefits are:

o Avoids paper work
o Cuts down operating expenses and excessive staffing
o Helps to rely on core business activities
o Promotes business as effectively as possible
o Systemizes company's data in simpler format
o Eliminates data redundancy
o Easy accessibility of data at any time

If you are planning to outsource your data conversion work, then you must choose the provider carefully in order to reap the fullest benefits of the services.

Data conversion experts at Managed Outsource Solutions (MOS) provides full conversion services of paper, microfilm, aperture cards, and large-scale drawings, through scanning, indexing, OCR, quality control and export of the archive and books to electronic formats or the final imaging solution. MOS is a US company providing managed outsource solutions that are focused on several industries, including medical, legal, information technology and media.




Source: http://ezinearticles.com/?Data-Conversion-Services&id=1523382

Unleash the Hidden Potential of Your Business Data With Data Mining and Extraction Services

Every business, small or large, is continuously amassing data about customers, employees and nearly every process in their business cycle. Although all management staff utilize data collected from their business as a basis for decision making in areas such as marketing, forecasting, planning and trouble-shooting, very often they are just barely scratching the surface. Manual data analysis is time-consuming and error-prone, and its limited functions result in the overlooking of valuable information that improve bottom-lines. Often, the sheer quantity of data prevents accurate and useful analysis by those without the necessary technology and experience. It is an unfortunate reality that much of this data goes to waste and companies often never realize that a valuable resource is being left untapped.

Automated data mining services allow your company to tap into the latent potential of large volumes of raw data and convert it into information that can be used in decision-making. While the use of the latest software makes data mining and data extraction fast and affordable, experienced professional data analysts are a key part of the data mining services offered by our company. Making the most of your data involves more than automatically generated reports from statistical software. It takes analysis and interpretation skills that can only be performed by experienced data analysis experts to ensure that your business databases are translated into information that you can easily comprehend and use in almost every aspect of your business.

Who Can Benefit From Data Mining Services?

If you are wondering what types of companies can benefit from data extraction services, the answer is virtually every type of business. This includes organizations dealing in customer service, sales and marketing, financial products, research and insurance.

How is Raw Data Converted to Useful Information?

There are several steps in data mining and extraction, but the most important thing for you as a business owner is to be assured that, throughout the process, the confidentiality of your data is our primary concern. Upon receiving your data, it is converted into the necessary format so that it can be entered into a data warehouse system. Next, it is compiled into a database, which is then sifted through by data mining experts to identify relevant data. Our trained and experienced staff then scan and analyze your data using a variety of methods to identify association or relationships between variables; clusters and classes, to identify correlations and groups within your data; and patterns, which allow trends to be identified and predictions to be made. Finally, the results are compiled in the form of written reports, visual data and spreadsheets, according to the needs of your business.

Our team of data mining, extraction and analyses experts have already helped a great number of businesses to tap into the potential of their raw data, with our speedy, cost-efficient and confidential services. Contact us today for more information on how our data mining and extraction services can help your business.




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Data Mining - A Short Introduction

Data mining is an integral part of data analysis which contains a series of activities that goes from the 'meaning' of the ideas, to the 'analysis' of the data and up to the 'interpretation' and 'evaluation' of the outcome. The different stages of the technique are as follows:

Objectives for Analysis: It is sometimes very difficult to statistically define the phenomenon we wish to analyze. In fact, the business objectives are often clear, but the same can be difficult to formalize. A clear understanding of the crisis and the goals is very important setup the analysis correctly. This is undoubtedly, one of the most complex parts of the process, since it establishes the techniques to be engaged and as such, the objectives must be crystal clear and there should not be any doubt or ambiguity.

Collection, grouping and pre-processing of the data: Once the objectives of the analysis are set and defined, we need to gather or choose the data needed for the study. At first, it is essential to recognize the data sources. Usually data are collected from the internal sources as the same are economical and more dependable and moreover these data also has the benefit of being the outcome of the experiences and procedures of the business itself.

Investigative analysis of the data and their conversion: This stage includes a preliminary examination of the information available. It involves a preliminary assessment of the significance of the gathered data. An exploratory and / or investigative analysis can highlight the irregular data. An exploratory analysis is important because it lets the analyst choose the most suitable statistical method for the subsequent stage of the analysis.

Choosing statistical methods: There are multiple statistical methods that can be put into use for the purpose of analysis, so it is very essential to categorize the existing methods. The choice statistical method is case specific and depends on the problem and also upon the type of information available.

Data analysis on the basis of chosen methods: Once the statistical method is chosen, the same must be translated into proper algorithms for working out the results. Ranges of specialized and non-specialized software are widely available for data mining and as such it is not always required to develop ad hoc computation algorithms for the most 'standard' purpose. However, it is essential that the people managing the data mining method well aware and have a good knowledge and understanding of the various methods of data analysis and also the different software solutions available for the same, so that they may adapt the same in times of need of the company and can flawlessly interpret the results.

Assessment and contrast of the techniques used and selection of the final model for analysis: It is of utmost necessity to choose the best 'model' from the variety of statistical methods accessible. The selection of the model should be based in contrast with the results obtained. When assessing the performance of a specific statistical method and / or type, all other dependent and / or relevant criterions should also be considered. The other criterions may be the constraints on the company both in terms of time and resources or it may be in terms of quality and the accessibility of data.

Elucidation of the selected statistical model and its employment in the decision making process: The scope of data mining is not limited to data analysis rather it is also includes the integration of the results so as to facilitate the decision making process of the company. Business awareness, the pulling out of rules and their use in the decision process allows us to proceed from the diagnostic phase to the phase of decision making. Once the model is finalized and tested with an information set, the categorization rule can be generalized. But the inclusion of the data mining process in the business should not be done in haste; rather the same should always be done slowly, setting out sensible and logical aims. The final aim of data mining is to be an integral supporting part of the company's decision making process.




Source: http://ezinearticles.com/?Data-Mining---A-Short-Introduction&id=6573285

What's Your Excuse For Not Using Data Mining?

In an earlier article I briefly described how data mining and RFM analysis can help marketers be more efficient (read... increased marketing ROI!). These marketing analytics tools can significantly help with all direct marketing efforts (multichannel campaign management efforts using direct mail, email and call center) and some interactive marketing efforts as well. So, why aren't all companies using it today? Well, typically it comes down to a lack of data and/or statistical expertise. Even if you don't have data mining expertise, YOU can benefit from data mining by using a consultant. With that in mind, let's tackle the first problem -- collecting and developing the data that is useful for data mining.

The most important data to collect for data mining include:

oTransaction data - For every sale, you at least need to know the product and the amount and date of the purchase.

oPast campaign response data - For every campaign you've run, you need to identify who responded and who didn't. You may need to use direct and indirect response attribution.

oGeo-demographic data - This is optional, but you may want to append your customer file/database with consumer overlay data from companies like Acxiom.

oLifestyle data - This is also an optional append of indicators of socio-economic lifestyle that are developed by companies like Claritas. All of the above data may or may not exist in the same data source. Some companies have a single holistic view of the customer in a database and some don't. If you don't, you'll have to make sure all data sources that contain customer data have the same customer ID/key. That way, all of the needed data can be brought together for data mining.

How much data do you need for data mining? You'll hear many different answers, but I like to have at least 15,000 customer records to have confidence in my results.

Once you have the data, you need to massage it to get it ready to be "baked" by your data mining application. Some data mining applications will automatically do this for you. It's like a bread machine where you put in all the ingredients -- they automatically get mixed, the bread rises, bakes, and is ready for consumption! Some notable companies that do this include KXEN, SAS, and SPSS. Even if you take the automated approach, it's helpful to understand what kinds of things are done to the data prior to model building.

Preparation includes:

oMissing data analysis. What fields have missing values? Should you fill in the missing values? If so, what values do you use? Should the field be used at all?

oOutlier detection. Is "33 children in a household" extreme? Probably - and consequently this value should be adjusted to perhaps the average or maximum number of children in your customer's households.

oTransformations and standardizations. When various fields have vastly different ranges (e.g., number of children per household and income), it's often helpful to standardize or normalize your data to get better results. It's also useful to transform data to get better predictive relationships. For instance, it's common to transform monetary variables by using their natural logs.

oBinning Data. Binning continuous variables is an approach that can help with noisy data. It is also required by some data mining algorithms.

More to come on data mining for marketers in my next article.

Jim Stafford has worked for leading companies in the Marketing Automation space (BI, data mining, campaign management and eMarketing) for over 10 years. He has held roles of Director - Database Marketing Solutions, Pre-Sales Manager, Product Manager, and Solution Architect at companies like Aprimo, Group1 Software, SAS, Siebel, SPSS and Unica. Mr. Stafford has consistently helped sales teams meet or beat established sales targets. He was the principal pre-sales contributor to Siebel's second largest MA sale with General Motors. Jim has had considerable exposure to many verticals including: Financial Services, Hospitality & Entertainment, Automotive, Communications, and Utilities. He is a seasoned expert at discovery and knows key industry trends. Jim has an M.A. Degree in Economics from the University of Maryland and has been a frequent speaker at annual National Center for Database Marketing and Direct Marketing Associations events. Visit [http://www.staffordsbsg.com/] to learn more about Jim and his company's services.




Source: http://ezinearticles.com/?Whats-Your-Excuse-For-Not-Using-Data-Mining?&id=3576029

Data Mining For Professional Service Firms - The Marketing Mother Lode May Already Be in Your Files

No one needs to tell you about the value of information in today's world--particularly the value of information that could help grow your practice. But has it occurred to you that you probably have more information in your head and your existing files that you realize? Tap into this gold mine of data to develop a powerful and effective marketing plan that will pull clients in the door and push your profitability up.

The way to do this is with data mining, which is the process of using your existing client data and demographics to highlight trends, make predictions and plan strategies.

In other words, do what other kinds of businesses have been doing for years: Analyze your clients by industry and size of business, the type and volume of services used, the amount billed, how quickly they pay and how profitable their business is to you. With this information, you'll be able to spot trends and put together a powerful marketing plan.

To data mine effectively, your marketing department needs access to client demographics and financial information. Your accounting department needs to provide numbers on the services billed, discounts given, the amounts actually collected, and receivables aging statistics. You may identify a specific service being utilized to a greater than average degree by a particular industry group, revealing a market segment worth pursuing. Or you may find an industry group that represents a significant portion of your billed revenue, but the business is only marginally profitable because of write-offs and discounts. In this case, you may want to shift your marketing focus.

You should also look at client revenues and profitability by the age of the clients. If your percentage of new clients is high, it could mean you're not retaining a sufficient number of existing clients. If you see too few new clients, you may be in for problems when natural client attrition is not balanced by new client acquisition.

The first step in effective data mining is to get everyone in the firm using the same information system. This allows everyone in the office who needs the names and addresses of the firm's clients and contacts to have access to that data. Require everyone to record notes on conversations and meetings in the system. Of course, the system should also accommodate information that users don't want to share, such as client's private numbers or the user's personal contacts. This way, everyone can utilize the system for everything, which makes them more likely to use it completely.

Your information system can be either contact information or customer relationship management software (a variety of packages are on the market) or you can have a system custom designed. When considering software to facilitate data mining, look at three key factors:

1. Ease of use. If the program isn't easy to use, it won't get used, and will end up being just a waste of time and money.

2. Accessibility. The system must allow for data to be accessible from anywhere, including laptops, hand-held devices, from the internet or cell phones. The data should also be accessible from a variety of applications so it can be used by everyone in the office all the time, regardless of where they are.

3. Sharability. Everyone needs to be able to access the information, but you also need privacy and editing rights so you can assign or restrict what various users can see and input.

Don't overlook the issue of information security. Beyond allowing people the ability to code certain entries as private, keep in mind that anyone with access to the system as the ability to either steal information or sabotage your operation. Talk to your software vendor about various security measures but don't let too much security make the system unusable. Protect yourself contractually with noncompete and nondisclosure agreements and be sure to back up your data regularly.

Finally, expect some staffers to resist when you ask them to change from the system they've been using. You may have to sell them on the benefits outweighing the pain of making a change and learning the new system--which means you need to be totally sold on it yourself. The managing partner, or the leader of the firm, needs to be driving this initiative for it to succeed. When it does succeed, you'll be able to focus your marketing dollars and efforts in the most profitable areas with the least expense, with a tremendous positive impact on the bottom line.

Jacquelyn Lynn is a business writer and ghostwriter based in Orlando, Florida. She is the author or ghostwriter of more than 25 books, including Entrepreneur's Almanac; Online Shopper's Survival Guide; Make Big Profits on eBay (with Charlene Davis); In Search of the Five-Cent Nickel (with Don Abbott); and 11 titles in Entrepreneur Media's StartUp Guide series.

Jacquelyn writes and ghostwrites a wide range of materials, including articles, newsletters, brochures, social media copy, blogs, website copy, books, ebooks, white papers, special reports, and more. Visit her website at http://www.jacquelynlynn.com to sign up to receive free business tips via email.




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The A B C D of Data Mining Services

If you are very new to the term 'data mining', let the meaning be explained to you. It is form of back office support services that are being offered by many call centers to analyze data from numerous resources and amalgamate them for some useful task. The business establishments in the present generation need to develop a strategy that helps them to cooperate with the market trends and allow them to perform well. The process of data mining is actually the retrieval process of essential and informative data that helps an organization to analyze the business perspectives and can further generate better interests in cutting cost, developing revenue and to acquire valuable data on business services/products.

It is a powerful analytical tool that permits the user to customize a wide range of data in different formats and categories as per their necessity. The data mining process is an integral part of a business plan for companies that need to undertake a diverse research on the customer building process. These analytical skills are generally performed by skilled industrial experts who assist the firms to accelerate their growth through the critical business activities. With a vast applicability in the present time, the back office support services with the data mining process is helping the businesses in understanding and predicting valuable information. Some of them include:

    Profiles of customers
    Customer buying behavior
    Customer buying trends
    Industry analysis

For a layman it is somewhat the process of processing some statistical data or methods. These processes are implemented with some specific tools that preform the following:

    Automated model scoring
    Business templates
    Computing target columns
    Database integration
    Exporting models to other applications
    Incorporating financial information

There are some benefits of Data Mining. Few of them are as follows:

    To understand the requirements of the customers which can help in efficient planning.
    Helps in minimizing risk and improve ROI.
    Generate more business and target the relevant market.
    Risk free outsourcing experience
    Provide data access to business analysts
    A better understanding of the demand supply graph
    Improve profitability by detect unusual pattern in sales, claims, transactions
    To cut down the expenses of Direct Marketing

Data mining is generally a part of the offshore back office services and outsourced to business establishments that require diverse data base on customers and their particular approach towards any service or product. For example banks, telecommunication companies, insurance companies, etc. require huge data base to promote their new policies. If you represent a similar company that needs appropriate data mining process then it is better that you outsource back office support services from a third party and fulfill your business goals with excellent results.

Katie Cardwell works as a senior sales and marketing analyst for a multinational call center company, based in United States of America. She takes care of all the business operations and analysis the back office support services that power an organization. Her extensive knowledge and expertise on Non -voice call center services such as Data Mining Services, Back office support services, etc, have helped many business players to stand with a straight spine and thus making a foothold in the data processing industry.




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Searching the Web Using Text Mining and Data Mining

There are many types of financial analysis tools that are useful for various purposes. Most of these are easily available online. Two such tools of software for financial analysis include the text mining and data mining. Both methods have been discussed in details in the following section.

The features of Text Mining It is a way by which information of high-quality can be derived from a text. It involves giving structure to the input text then deriving patterns within the data that has been structured. Finally, the process of evaluating and interpreting the output is undertaken.

This form of mining usually involves the process of structuring the text input, and deriving patterns within the structured data, and finally evaluating and interpreting the data. It differs from the way we are familiar with in searching the web. The goal of this method is to find unknown information. It can be done with analyses in topics that that were not researched before.

What is Data Mining? It is the process of the extraction of patterns from the data. Nowadays, it has become very vital to transform this data into information. It is particularly used in marketing practices as well as fraud detection and surveillance. We can extract hidden information from huge databases of information. It can be used to predict future trends as well as to aid the company business to make knowledgeable quick decisions.

Working of data mining: Modeling technique is used to perform the operation of such form of mining. For these techniques, you must need to be fully integrated with a data warehouse as well as financial analysis tools. Some of the areas where this method is used are:

    Pharmaceutical companies which need to analyze its sales force and to achieve their targets.
    Credit card companies and transportation companies with sales force.
    Also large consumer goods companies use such mining techniques.
    With this method, a retailer may utilize POS or point-of-sale data of customer purchases in order to develop strategies for sale promotion.

The major elements of Data mining:

1. Extracting, transforming, and sending load transaction data on the data warehouse of the server system.

2. Storing and managing the data in for database systems that are multidimensional in nature.

3. Presenting data to the IT professionals and business analysts for processing.

4. Presenting the data to the application software for analyses.

5. Presentation of the data in dynamic ways like graph or table.

The main point of difference between the two types of mining is that text mining checks the patterns from natural text instead of databases where the data is structured.

Data mining software supports the entire process of such mining and discovery of knowledge. These are available on the internet. Data mining software serves as one of the best financial analysis tools. You can avail of data mining software suites and their reviews freely over the internet and easily compare between them.




Source: http://ezinearticles.com/?Searching-the-Web-Using-Text-Mining-and-Data-Mining&id=5299621

Various Data Mining Techniques

Also called Knowledge Discover in Databases (KDD), data mining is the process of automatically sifting through large volumes of data for patterns, using tools such as clustering, classification, association rule mining, and many more. There are several major data mining techniques developed and known today, and this article will briefly tackle them, along with tools for increased efficiency, including phone look up services.

Classification is a classic data mining technique. Based on machine learning, it is used to classify each item on a data set into one of predefined set of groups or classes. This method uses mathematical techniques, like linear programming, decision trees, neural network, and statistics. For instance, you can apply this technique in an application that predicts which current employees will most probably leave in the future, based on the past records of those who have resigned or left the company.

Association is one of the most used techniques, and it is where a pattern is discovered basing on a relationship of a specific item on other items within the same transaction. Market basket analysis, for example, uses association to figure out what products or services are purchased together by clients. Businesses use the data produced to devise their marketing campaign.

Sequential patterns, too, aim to discover similar patterns in data transaction over a given business phase or period. These findings are used for business analysis to see relationships among data.

Clustering makes useful cluster of objects that maintain similar characteristics using an automatic method. While classification assigns objects into predefined classes, clustering defines the classes and puts objects in them. Predication, on the other hand, is a technique that digs into the relationship between independent variables and between dependent and independent variables. It can be used to predict profits in the future - a fitted regression curve used for profit prediction can be drawn from historical sale and profit data.

Of course, it is highly important to have high-quality data in all these data mining techniques. A multi-database web service, for instance, can be incorporated to provide the most accurate telephone number lookup. It delivers real-time access to a range of public, private, and proprietary telephone data. This type of phone look up service is fast-becoming a defacto standard for cleaning data and it communicates directly with telco data sources as well.

Phone number look up web services - just like lead, name, and address validation services - help make sure that information is always fresh, up-to-date, and in the best shape for data mining techniques to be applied.



Source: http://ezinearticles.com/?Various-Data-Mining-Techniques&id=6985662

Text Data Mining Can Be Profitable

There are billions of search terms performed on the internet every year,and the companies which make use of this vast amount of information are the ones who will be able to market effectively in the future. It is here that text data mining comes into its own, a technique which enables researchers to find patterns within groups of text which will enable them to make predictions as to how customers or other groups of people will act in the future. This article will take a look at text data mining and how we can help various groups of people to find the best things in the data analysis.

It is always a good idea to do some study of the text mining techniques before going on to text mining implementation, and this can be said to be especially true of the insurance industry where not only text mining but also generic data mining using in statistics can be a great help in determining profitability and also showing actuaries how to make future calculations.

Consultancy is an important part of text data mining, and the text mining consultant can bring a huge amount of knowledge to a company whatever the service or services that are providing, particularly if he has an extensive knowledge of text data mining technology and can help to build a system around it.

Of course it is not only commercial applications that can use text mining, because it also has used in security, in that it can help to track criminal intent on the Internet. There are also applications in the biomedical world, in order to help find clusters of data in the right way. But it is in the online world and in the field of marketing that text mining is being used extensively, particularly in customer relationship management [CRM] techniques, where the tools are among some of the most advanced.

Knowing how text mining algorithms work is essential for any consultant who works in this field, because it is an important tool in the marketing technique possibilities. By understanding how text data mining can help an organization a consultant or marketer can make great strides in profitability and this is something that most organizations would be glad for.



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Spatial Data Mining Systems

Data mining systems are used for a variety of different purposes. Essentially, large amounts of data are stored in one particular spot, enabling organizations and companies to access information that will help them in their own marketing and surveillance strategies. By having access to all relevant data, a company can better employ their sales and production tactics. Companies and businesses can save large sums of money by researching past consumer behaviors and producing product in relation to how well it sold at certain times. This is just a small example of what data mining can do for a company.

Spatial data mining systems rely on the same principals. However, the data stored is related directly to special data. Spatial data mining systems are also used to detect patterns, but the patterns that are being looked for are geographical patterns. Up until this point geographical information systems and spatial data mining have existed as two separate technologies. Both systems have their own individual approaches to storing geographical data. Each system has derived from its own methods and traditions, making it difficult to cross the two. Geographical information systems tend to be much more basic and only provide the most simple form of functionality. Because there became a larger demand for geographically referenced data, the basic functions of GIS represented the massive need for more sophisticated methods of mining spatial data. There is a larger demand for geographical analysis and modeling as well as digital mapping and remote sensing.

Through spatial data mining, there have been numerous benefits experienced by those who make important decisions based on geographical information systems. Public and private sector organizations have recently become aware of the huge potential of the amount of information they possess in their thematic and geographical referenced databases. There are various types of companies who can benefit from geographical data. For example, those that are in the public health sector will use this data to determine the cause for epidemics such as disease clusters. In addition, some environmental agencies will use the information collected in these databases to understand the impact of land-use patterns that are in constant flux and how they relate to climate change. Geo-marketing companies will also find this information useful when they are conducting customer research regarding segmentation on spatial location.

However, spatial data mining systems force those who need them to face certain challenges. First of all, these databases tend to be extremely large and can be cumbersome to sort through when looking for specific information. Geographical information system datasets that already exist are usually split into featured and attributed components and this means that they are separated into hybrid data management systems. Both featured and attributed data systems require separate means of management. For example algorithmic requirements differ when it comes to relational data, which is in the attribute category and for topographical data, which falls under the feature category.

The two main systems for spatial data management are the raster and the vector. Depending on the needs of the data being used, it is important to analyze the benefits and downfalls of both systems.

Doing business in the 21st century doesn't have to be difficult - companies can enhance their marketing procedures through address validation software and various other list cleaning procedures so that they can target their market perfectly!



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Importance of Data Mining Services in Business

Data mining is used in re-establishment of hidden information of the data of the algorithms. It helps to extract the useful information starting from the data, which can be useful to make practical interpretations for the decision making.
It can be technically defined as automated extraction of hidden information of great databases for the predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making. Although data mining is a relatively new term, the technology is not. It is thus also known as Knowledge discovery in databases since it grip searching for implied information in large databases.
It is primarily used today by companies with a strong customer focus - retail, financial, communication and marketing organizations. It is having lot of importance because of its huge applicability. It is being used increasingly in business applications for understanding and then predicting valuable data, like consumer buying actions and buying tendency, profiles of customers, industry analysis, etc. It is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, e-commerce, customer relationship management and financial services.

However, the use of some advanced technologies makes it a decision making tool as well. It is used in market research, industry research and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, scientific tests, genetics, financial services and utilities.

Data mining consists of major elements:

    Extract and load operation data onto the data store system.
    Store and manage the data in a multidimensional database system.
    Provide data access to business analysts and information technology professionals.
    Analyze the data by application software.
    Present the data in a useful format, such as a graph or table.

The use of data mining in business makes the data more related in application. There are several kinds of data mining: text mining, web mining, relational databases, graphic data mining, audio mining and video mining, which are all used in business intelligence applications. Data mining software is used to analyze consumer data and trends in banking as well as many other industries.



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Data Mining Basics

Definition and Purpose of Data Mining:

Data mining is a relatively new term that refers to the process by which predictive patterns are extracted from information.

Data is often stored in large, relational databases and the amount of information stored can be substantial. But what does this data mean? How can a company or organization figure out patterns that are critical to its performance and then take action based on these patterns? To manually wade through the information stored in a large database and then figure out what is important to your organization can be next to impossible.

This is where data mining techniques come to the rescue! Data mining software analyzes huge quantities of data and then determines predictive patterns by examining relationships.

Data Mining Techniques:

There are numerous data mining (DM) techniques and the type of data being examined strongly influences the type of data mining technique used.

Note that the nature of data mining is constantly evolving and new DM techniques are being implemented all the time.

Generally speaking, there are several main techniques used by data mining software: clustering, classification, regression and association methods.

Clustering:

Clustering refers to the formation of data clusters that are grouped together by some sort of relationship that identifies that data as being similar. An example of this would be sales data that is clustered into specific markets.

Classification:

Data is grouped together by applying known structure to the data warehouse being examined. This method is great for categorical information and uses one or more algorithms such as decision tree learning, neural networks and "nearest neighbor" methods.

Regression:

Regression utilizes mathematical formulas and is superb for numerical information. It basically looks at the numerical data and then attempts to apply a formula that fits that data.

New data can then be plugged into the formula, which results in predictive analysis.

Association:

Often referred to as "association rule learning," this method is popular and entails the discovery of interesting relationships between variables in the data warehouse (where the data is stored for analysis). Once an association "rule" has been established, predictions can then be made and acted upon. An example of this is shopping: if people buy a particular item then there may be a high chance that they also buy another specific item (the store manager could then make sure these items are located near each other).

Data Mining and the Business Intelligence Stack:

Business intelligence refers to the gathering, storing and analyzing of data for the purpose of making intelligent business decisions. Business intelligence is commonly divided into several layers, all of which constitute the business intelligence "stack."

The BI (business intelligence) stack consists of: a data layer, analytics layer and presentation layer.

The analytics layer is responsible for data analysis and it is this layer where data mining occurs within the stack. Other elements that are part of the analytics layer are predictive analysis and KPI (key performance indicator) formation.

Data mining is a critical part of business intelligence, providing key relationships between groups of data that is then displayed to end users via data visualization (part of the BI stack's presentation layer). Individuals can then quickly view these relationships in a graphical manner and take some sort of action based on the data being displayed.



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How Data Mining Can Help in Customer Relationship Management Or CRM?

Customer relationship management (CRM) is critical activity of improvising customer interactions while at the same time making the interactions more amicable through individualization. Data mining utilizes various data analysis and modeling methods to detect specific patterns and relationships in data. This helps in understanding what a customer wants and forecasting what they will do.

Using Data mining you can find out right prospects and offer them right products. This results in improved revenue because you can respond to each customer in best way using fewer resources.

Basic process of CRM data mining includes:
1. Define business objective
2. Construct marketing database
3. Analyze data
4. Visualize a model
5. Explore model
6. Set up model & start monitoring

Let me explain above steps in detail.

Define the business objective:
Every CRM process has one or more business objective for which you need to construct the suitable model. This model varies depending on your specific goal. The more precise your statement for defining the problem is the more successful is your CRM project.

Construct a marketing database:
This step involves creation of constructive marketing database since your operational data often don't contain the information in the form you want it. The first step in building your database is to clean it up so that you can construct clean models with accurate data.

The data you need may be scattered across different databases such as the client database, operational database and sales databases. This means you have to integrate the data into a single marketing database. Inaccurately reconciled data is a major source of quality issues.

Analyze the data:
Prior to building a correct predictive model, you must analyze your data. Collect a variety of numerical summaries (such as averages, standard deviations and so forth). You may want to generate a cross-section of multi-dimensional data such as pivot tables.

Graphing and visualization tools are a vital aid in data analysis. Data visualization most often provides better insight that leads to innovative ideas and success.



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Beneficial Data Collection Services

Internet is becoming the biggest source for information gathering. Varieties of search engines are available over the World Wide Web which helps in searching any kind of information easily and quickly. Every business needs relevant data for their decision making for which market research plays a crucial role. One of the services booming very fast is the data collection services. This data mining service helps in gathering relevant data which is hugely needed for your business or personal use.

Traditionally, data collection has been done manually which is not very feasible in case of bulk data requirement. Although people still use manual copying and pasting of data from Web pages or download a complete Web site which is shear wastage of time and effort. Instead, a more reliable and convenient method is automated data collection technique. There is a web scraping techniques that crawls through thousands of web pages for the specified topic and simultaneously incorporates this information into a database, XML file, CSV file, or other custom format for future reference. Few of the most commonly used web data extraction processes are websites which provide you information about the competitor's pricing and featured data; spider is a government portal that helps in extracting the names of citizens for an investigation; websites which have variety of downloadable images.

Aside, there is a more sophisticated method of automated data collection service. Here, you can easily scrape the web site information on daily basis automatically. This method greatly helps you in discovering the latest market trends, customer behavior and the future trends. Few of the major examples of automated data collection solutions are price monitoring information; collection of data of various financial institutions on a daily basis; verification of different reports on a constant basis and use them for taking better and progressive business decisions.

While using these service make sure you use the right procedure. Like when you are retrieving data download it in a spreadsheet so that the analysts can do the comparison and analysis properly. This will also help in getting accurate results in a faster and more refined manner.



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Data Mining Services

You will get all solutions regarding data mining from many companies in India. You can consult a variety of companies for data mining services and considering the variety is beneficial to customers. These companies also offer web research services which will help companies to perform critical business activities.

Very competitive prices for commodities will be the results where there is competition among qualified players in the data mining, data collection services and other computer-based services. Every company willing to cut down their costs regarding outsourcing data mining services and BPO data mining services will benefit from the companies offering data mining services in India. In addition, web research services are being sourced from the companies.

Outsourcing is a great way to reduce costs regarding labor, and companies in India will benefit from companies in India as well as from outside the country. The most famous aspect of outsourcing is data entry. Preference of outsourcing services from offshore countries has been a practice by companies to reduce costs, and therefore, it is not a wonder getting outsource data mining to India.

For companies which are seeking for outsourcing services such as outsource web data extraction, it is good to consider a variety of companies. The comparison will help them get best quality of service and businesses will grow rapidly in regard to the opportunities provided by the outsourcing companies. Outsourcing does not only provide opportunities for companies to reduce costs but to get labor where countries are experiencing shortage.

Outsourcing presents good and fast communication opportunity to companies. People will be communicating at the most convenient time they have to get the job done. The company is able to gather dedicated resources and team to accomplish their purpose. Outsourcing is a good way of getting a good job because the company will look for the best workforce. In addition, the competition for the outsourcing provides a rich ground to get the best providers.

In order to retain the job, providers will need to perform very well. The company will be getting high quality services even in regard to the price they are offering. In fact, it is possible to get people to work on your projects. Companies are able to get work done with the shortest time possible. For instance, where there is a lot of work to be done, companies may post the projects onto the websites and the projects will get people to work on them. The time factor comes in where the company will not have to wait if it wants the projects completed immediately.

Outsourcing has been effective in cutting labor costs because companies will not have to pay the extra amount required to retain employees such as the allowances relating to travels, as well as housing and health. These responsibilities are met by the companies that employ people on a permanent basis. The opportunity presented by the outsourcing of data and services is comfort among many other things because these jobs can be completed at home. This is the reason why the jobs will be preferred more in the future.



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