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Data Mining

Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from statistics and pattern recognition. Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data". Although it is usually used in relation to analysis of data, data mining, like artificial intelligence, is an umbrella term and is used with varied meanings in a wide range of contexts. It is usually associated with a business or other organization's need to identify trends.

A simple example of data mining is its use in a retail sales department. If a store tracks the purchases of a customer and notices that a customer buys a lot of silk shirts, the data mining system will make a connection sport ween that customer and silk shirts. The sales department will look at that information and may begin direct mail marketing of silk shirts to that customer, or may alternatively attempt to get the customer to buy a wider range of products. In this case, the data mining system used by the retail store discovered new information about the customer that was previously unknown to the company.

Another widely used hypothetical example is that of a very large North American chain of supermarkets. Through intensive analysis of the purchases made and the goods bought over a period of time, analysts found that beers and diapers were often bought together. Though explaining this might be difficult, taking advantage of it, on the other hand, should not be hard (e.g. placing the high-profit diapers next to the high-profit beers).

Introduction todata mining

What is data mining ?

Data Mining is a systematic process designed to look for hidden patterns after exploring data (usually large amounts of data, generally business or market related) in search of dependable patterns and/or analytic relationships between items or variables, and then use them to validate these conclusions by applying the detected patterns to new subsets of data. The fundamental asset of data mining is prediction - and predictive data mining is the most used category of data mining and the one that is used by server businesses as a statistical tool. For example a supermarket can use data mining to find its customers baster behaviour pattern through products bought. Many companies use data mining as a technique for accumulative statistical purposes and to be able to utilize data mining properly one must use data mining software and data mining applications.

Data mining Software
Data mining software allows you to collect and modify your current variable which could be customers for higher levels of customer recollection and allegiance to your company. Firstly, you build a customer database which is very easy.Then, you expand your customer information beyond contact data to include variables like purchasing data mining statistics, marketing data mining statistics, and demographic data mining statistics. Leave your competitors behind by using an affordable desktop database solution.

Microsoft Access as a data mining software tool
Access is a fairly new entry to the Microsoft set of products. Access provides a small business the ability to organize and manage data for customer data mining and other data base solutions. Create a variety of reports and forms in your data base to connect with your customers and drive further profits. Excellent for growing company environments. Newer versions of the software provides users with data support for XML, OLE, and ODBC.

FileMaker Pro as a data mining software tool
FileMaker Pro a stand alone data mining application, continues its product leadership in the desktop database category. For the technically challenged small business, FileMaker Pro provides an easy-to-use format to maximize your customer information. Database templates to get you started are a nice must-have feature. An added security function allows limited access to employees and read-only formats necessary to protect private information.

Lotus approach as a data mining software tool
Lotus Approach allows full modification and statistical analysis of your customer data. This database data mining tool can integrate items like forms, worksheets, and charts in Lotus Notes mail. Lotus Approach provides an alternative to other data mining products.

What is different between statistics and data mining?

There is no easy answer as to was is the difference between "data mining" and statistics are.
The techniques and concepts used in data mining are basically the same as those used in statistics. For example both techniques are used to predict data or classify data and arrange them in catagories. One must know that many data mining techniques were previously used in statistics like CART or CHAID.
But now let's get to the differences between "data mining" and statistics.
One gets very excited when he hears the word "data mining" but the same doesn't happen with "statistics". This happens mainly because data mining techniques are friendlier and better collectors of data for the average user.
Another reason, of course, is the current technology the world has come into. The use of computers for business data storage has changed things in a way that the amount of data that can be stored in smaller spaces is vaster and more available to the users than ever before. If there was no data we wouldn't have to mine it. And as computers grow stronger each day, data mining becomes more powerful and the computers technique is the one we keep in our head when we hear data mining. But the differences between "data mining" and statistic are not many.

Data mining concepts & techniques

Nearest Neighbor and clustering data mining

Clustering and the Nearest Neighbor prediction technique are one of the chronologically oldest techniques used in data mining. Most people have an instinct that they comprehend what clustering is - namely that like records are grouped or categorized together. Nearest neighbor is a data mining technique that is quite similar to clustering - its basis is that in order to predict what a prediction value is in one record look for records with similar predictor values in the historical database and use the prediction value from the record that it “nearest” to the unclassified record.

A simple example of clustering data mining would be the clustering that most people perform when they do the laundry - grouping the permanent press, dry cleaning, whites and brightly colored clothes is important because they have similar characteristics. And it turns out they have important attributes in common about the way they behave (and can be ruined) in the wash. To cluster your laundry most of your decisions are relatively straightforward. There are of course difficult decisions to be made about which cluster your white shirt with red stripes goes into (since it is mostly white but has some color and is permanent press). When clustering is used in business the clusters are often much more dynamic - even changing weekly to monthly and many more of the decisions concerning which cluster a record falls into can be difficult.

Data preparation for data mining
Years of practical experience of data mining and its statistical analyses have shown that
preparing data is the most time-consuming part of any data mining project. Estimates of the amount of timeand resources spent on data preparation vary from atleast 60% to upward of 80% (SPSS, 2002a). In spite of this fact, not enough attention is given to this important task, thus perpetuating the idea that the core of the data mining effort is the modeling process rather than all phases of the data mining life cycle. This article presents a synopsis of the most crucial issues and considerations a company should take for data mining.

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Data Mining Article by Panos
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