Understanding data mining and predictive analytics
This information is a summary of the material available on Wikipedia. For more information about data mining and predictive analytics, see the following pages:
http://en.wikipedia.org/wiki/Data_mining
http://en.wikipedia.org/wiki/Predictive_analytics
Data mining is the process of finding patterns in large data sets. Data mining includes:
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BIRT Analytics supports association rule learning, clustering, and classification (decision trees). BIRT Analytics also supports using time-series prediction to produce short-term demand forecasts. For example, your sales data may contain a trend or a seasonal pattern.
Patterns identified by data mining can be further analyzed. A type of analysis that is of particular interest in business applications is predictive analytics. Predictive analytics is the process of analyzing data to make predictions about future or unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals, and other fields. In financial services, for example, credit scoring is a very common application of predictive analytics. Scoring models analyze a borrower’s credit history in order to rank borrowers by the likelihood that they will repay loans on time. One of the most widely used credit scores is the FICO score. Other applications of predictive analytics include:
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