caa a22 4500 445871245 CHVBK 20180317145512.0 cr unu---uuuuu 170323e20110701xx s 000 0 eng 10.1007/s10618-010-0202-x doi (NATIONALLICENCE)springer-10.1007/s10618-010-0202-x Krimp [Elektronische Daten] mining itemsets that compress [Jilles Vreeken, Matthijs van Leeuwen, Arno Siebes] 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. The Author(s), 2010 MDL nationallicence Pattern mining nationallicence Pattern selection nationallicence Itemsets nationallicence Vreeken Jilles Algorithmic Data Analysis, Department of Information and Computing Sciences, Faculty of Science, Universiteit Utrecht, Utrecht, The Netherlands aut van Leeuwen Matthijs Algorithmic Data Analysis, Department of Information and Computing Sciences, Faculty of Science, Universiteit Utrecht, Utrecht, The Netherlands aut Siebes Arno Algorithmic Data Analysis, Department of Information and Computing Sciences, Faculty of Science, Universiteit Utrecht, Utrecht, The Netherlands aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/1(2011-07-01), 169-214 1384-5810 23:1<169 2011 23 10618 https://doi.org/10.1007/s10618-010-0202-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0202-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Vreeken Jilles Algorithmic Data Analysis, Department of Information and Computing Sciences, Faculty of Science, Universiteit Utrecht, Utrecht, The Netherlands aut NATIONALLICENCE 700 1- van Leeuwen Matthijs Algorithmic Data Analysis, Department of Information and Computing Sciences, Faculty of Science, Universiteit Utrecht, Utrecht, The Netherlands aut NATIONALLICENCE 700 1- Siebes Arno Algorithmic Data Analysis, Department of Information and Computing Sciences, Faculty of Science, Universiteit Utrecht, Utrecht, The Netherlands aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/1(2011-07-01), 169-214 1384-5810 23:1<169 2011 23 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer