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Abstract

A data-centric data mining technique provides greater ease of use and flexibility, yet provides high quality data mining results by providing general methodologies for automatic data mining. A methodology for each major type of mining function is provided, including: supervised modeling (classification and regression), feature selection, and ranking, clustering, outlier detection, projection of the data to lower dimensionality, association discovery, and data source comparison. A method for data-centric data mining comprises invoking a data mining feature to perform data mining on a data source, performing data mining on data from the data source using the data mining feature, wherein the data mining feature uses data mining processes and objects internal to the data mining feature and does not use data mining processes and objects external to the data mining feature, outputting data mining results from the data mining feature, and removing all data mining processes and objects internal to the data mining feature that were used to process the data from the data source.

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