Title
KRIMP : mining itemsets that compress KRIMP : mining itemsets that compress
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Boston, Mass. ,
Subject
Computer. Automation
Source (journal)
Data mining and knowledge discovery. - Boston, Mass.
Volume/pages
23(2011) :1 , p. 169-214
ISSN
1384-5810
ISI
000289106000005
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
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.
E-info
https://repository.uantwerpen.be/docman/iruaauth/cbc566/4382898.pdf
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