Understanding data mining and predictive analytics

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. This information can be converted into knowledge about historical patterns and future trends. For example, sales information can be analyzed in light of promotional efforts to provide knowledge of future consumer buying behavior.

Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. It involves the following processes:

Association rule learning: The identification of relationships between variables. For example, association rule learning can help a supermarket to determine which products are frequently bought together through the analysis of its point-of-sale data.

Clustering: Discovering groups and structures in the data that are similar.

Classification: Determining the class of an object based on its attributes. For example, an email program classifies e-mails as legitimate or as spam.

Correlation and Regression: Finding data relationships and applying functions that model the data with the least error.

Summarization: Providing a more compact representation of the data set, including visualization and report generation.

Sequential pattern mining and prediction: Finding sets of data items that occur together frequently in some sequences. Sequential pattern mining is the basis for web user analysis, stock trend prediction, DNA sequence analysis or using a history of symptoms to predict disease.

BIRT Analytics harnesses all these processes to provide a complete analysis of your data. Its predictive analytics functions enable, among others, time-series predictions and the production of short-term demand forecasts.

The use of predictive analytics has been very useful in the following domains:

For more information about data mining and predictive analytics, see the following pages: