Data-centric automatic data mining

A computer program product comprising a computer-readable storage medium comprising computer program instructions that when executed by a processor implement a method for data-centric data mining comprising:

• selecting a data mining feature from a provided group of data mining features to perform data mining on a data source;
• performing data mining on data from the data source using the selected 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, and wherein the processing performed within the data mining feature comprises automatically determining a type of data mining technique, automatically determining whether the data mining processing is a supervised problem, automatically selecting an algorithm for a data mining model, automatically preparing the data from the data source for the use with the selected algorithm, automatically tuning settings of the data mining model, and automatically applying the data mining model to the data from the data source to generate data mining results;
• outputting data mining results from the selected data mining feature;
• and
• removing all data mining processes and objects internal to the selected data mining feature that were used to process the data from the data source,
• wherein the provided group of data mining features comprises:
• a data mining feature that identifies attributes that are important or useful for explaining variation on a target attribute of interest and that provides a ranking of the attributes and a figure of merit;
• a data mining feature that produces predictions for unknown targets for classification mining tasks and regression mining tasks, and a figure of merit indicating the overall quality of the predictions;
• a data mining feature that clusters similar records together and provides description for the groups;
• a data mining feature that identifies records in the data from the data source that are atypical, outliers, or anomalies;
• a data mining feature that projects the data from the data source to a space of lower dimensionality and provides description of newly created attributes;
• a data mining feature that uncovers hidden patterns in the data from the data source and provides a list of discovered patterns;
• and
• a data mining feature that compares content of two data sources and produces a summary of differences and similarities discovered.