A Dataset Decomposition Approach to Data Mining and Machine Discovery

Blaz Zupan, Marko Bohanec, Ivan Bratko, and Bojan Cestnik

We present a novel data mining approach based on decomposition. In order to analyze a given dataset, the method decomposes it to a hierarchy of smaller and less complex datasets that can be analyzed independently. The method is experimentally evaluated on a real-world housing loans allocation dataset, showing that the decomposition can (1) discover meaningful intermediate concepts, (2) decompose a relatively complex dataset to datasets that are easy to analyze and comprehend, and (3) derive a classifier of high classification accuracy. We also show that human interaction has a positive effect on both the comprehensibility and classification accuracy.


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