Krimp: mining itemsets that compress


One of the major problems in pattern mining is the explosion of the number of results. Tight constraints reveal only common knowledge, while loose constraints lead to an explosion in the number of returned patterns. This is caused by large groups of patterns essentially describing the same set of transactions. In this paper we approach this problem using the MDL principle: the best set of patterns is that set that compresses the database best. For this task we introduce the Krimp algorithm. Experimental evaluation shows that typically only hundreds of itemsets are returned; a dramatic reduction, up to seven orders of magnitude, in the number of frequent item sets. These selections, called code tables, are of high quality. This is shown with compression ratios, swap-randomisation, and the accuracies of the code table-based Krimp classifier, all obtained on a wide range of datasets. Further, we extensively evaluate the heuristic choices made in the design of the algorithm.


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Jilles Vreeken is supported by the NWO project Mining Factors of Celiac Disease, part of the Computational Life Sciences Programme. Matthijs van Leeuwen is supported by the NBIC Biorange Programme and the NWO project Exceptional Model Mining, under number 612.065.822. The authors would like to thank Sander Schuckmann for parallelising the Krimp implementation.

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Correspondence to Jilles Vreeken.

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The research described in this paper builds upon and extends the work appearing in SDM’06 (Siebes et al. 2006) and ECML PKDD’06 (van Leeuwen et al. 2006).

Responsible editor: M.J. Zaki.

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Vreeken, J., van Leeuwen, M. & Siebes, A. Krimp: mining itemsets that compress. Data Min Knowl Disc 23, 169–214 (2011).

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  • MDL
  • Pattern mining
  • Pattern selection
  • Itemsets