How can one determine whether a data mining method extracts interesting patterns? The paper deals with this core question in the context of unsupervised problems with binary data. We formalize the quality of a data mining method by identifying patterns – the supporters and opponents – which are related to a pattern extracted by a method. We define a typology offering a global picture of the methods based on two complementary criteria to evaluate and interpret their interests. The quality of a data mining method is quantified via an evaluation complexity analysis based on the number of supporters and opponents of a pattern extracted by the method. We provide an experimental study on the evaluation of the quality of the methods.
CITATION STYLE
Crémilleux, B., Giacometti, A., & Soulet, A. (2019). How your supporters and opponents define your interestingness. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11051 LNAI, pp. 373–389). Springer Verlag. https://doi.org/10.1007/978-3-030-10925-7_23
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