DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree

Zulkurnain , N.F. and Haglin, David J. and Keane, John A (2013) DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree. In: Emerging Trends in Knowledge Discovery and Data Mining. Lecture Notes in Artificial Intelligence (7769). Springer Berlin Heidelberg, pp. 141-156. ISBN 978-3-642-36777-9

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Abstract

A recent focus in itemset mining has been the discovery of frequent itemsets from high-dimensional datasets. With exponentially increasing running time as average row length increases, mining such datasets renders most conventional algorithms impractical. Unfortunately, large cardinality itemsets are likely to be more informative than small cardinality itemsets in this type of dataset. This paper proposes an approach, termed DisClose, to extract large cardinality (colossal) closed itemsets from high-dimensional datasets. The approach relies on a Compact Row-Tree data structure to represent itemsets during the search process. Large cardinality itemsets are enumerated first followed by smaller ones. In addition, we utilize a minimum cardinality threshold to further reduce the search space. Experimental results show that DisClose can achieve extraction of colossal closed itemsets in the discovered datasets, even for low support thresholds. The algorithm immediately discovers closed itemsets without needing to check if each new closed itemset has previously been found.

Item Type:	Book Chapter
Additional Information:	4123/51446
Uncontrolled Keywords:	Colossal closed itemset, high-dimensional dataset, minimum cardinality threshold.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	DR Nurul Fariza Zulkurnain
Date Deposited:	08 Aug 2016 11:43
Last Modified:	08 Aug 2016 11:43
URI:	http://irep.iium.edu.my/id/eprint/51446

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