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Data mining applications extract patterns from stored data, used for marketing, fraud detection, and scientific research. Applications are often industry-specific, such as banking or government agencies. Pattern mining detects industry-specific patterns, and the value of data mining is high for businesses with large amounts of stored data.
Data mining applications are computer programs or software packages that allow patterns to be extracted and identified from stored data. This type of tool is typically a software interface that interacts with a large database containing customer data or other important data. Data mining is widely used by businesses and government agencies for uses such as marketing, fraud detection, and scientific research.
A wide variety of data mining applications are available, especially for business uses, such as Customer Relationship Management (CRM). These applications allow marketing managers to understand the behaviors of their customers and also to predict the potential behavior of prospects. One example of the type of business a data mining technique can help with is predicting future customer retention. For example, a business might decide to raise its prices and might use data mining to predict how many customers might be lost due to a particular percentage increase in product price.
Data mining applications are often structured around the specific needs of an industry sector or even customized and built for a single organization. This is because the patterns within the data can be very specific. For example, banking data mining applications may need to track customers’ spending habits in order to detect unusual transactions that may be fraudulent. In another example, an application might be used by a government agency to detect associations between individuals who may be involved in terrorist activity.
Pattern mining is a term sometimes used to refer to the detection of industry-specific patterns in particular types of data. Using this technique, data mining association rules can be found that can give a probability that one characteristic or behavior is associated with another. An example of a data mining association rule found by a data mining application analyzing data for a supermarket might be, for example, the knowledge that pasta and sauce are purchased together 90% of the time.
The value of data mining applications in enterprises is often estimated to be extremely high. Some companies have stored large amounts of data over years of operation, but without proper application they are missing out on the very valuable information that may be contained in their existing data. Installing and using data mining applications can sometimes be an investment that quickly pays dividends by allowing a business to leverage existing information into more customers, more sales, or higher profits.
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