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

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 ar...

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Main Authors:	Zulkurnain , N.F., Haglin, David J., Keane, John A
Format:	Book Chapter
Language:	English English
Published:	Springer Berlin Heidelberg 2013
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/51446/ http://irep.iium.edu.my/51446/ http://irep.iium.edu.my/51446/ http://irep.iium.edu.my/51446/1/DisClose_2013.pdf http://irep.iium.edu.my/51446/4/51446-DisClose_Discovering_Colossal_Closed_Itemsets-SCOPUS.pdf

id	iium-51446
recordtype	eprints
spelling	iium-514462016-08-08T03:43:07Z http://irep.iium.edu.my/51446/ DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree Zulkurnain , N.F. Haglin, David J. Keane, John A TK7885 Computer engineering 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. Springer Berlin Heidelberg 2013 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/51446/1/DisClose_2013.pdf application/pdf en http://irep.iium.edu.my/51446/4/51446-DisClose_Discovering_Colossal_Closed_Itemsets-SCOPUS.pdf 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 http://www.springer.com/us/book/9783642367779# 10.1007/978-3-642-36778-6
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Zulkurnain , N.F. Haglin, David J. Keane, John A DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree
description	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.
format	Book Chapter
author	Zulkurnain , N.F. Haglin, David J. Keane, John A
author_facet	Zulkurnain , N.F. Haglin, David J. Keane, John A
author_sort	Zulkurnain , N.F.
title	DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree
title_short	DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree
title_full	DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree
title_fullStr	DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree
title_full_unstemmed	DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree
title_sort	disclose: discovering colossal closed itemsets via a memory efficient compact row-tree
publisher	Springer Berlin Heidelberg
publishDate	2013
url	http://irep.iium.edu.my/51446/ http://irep.iium.edu.my/51446/ http://irep.iium.edu.my/51446/ http://irep.iium.edu.my/51446/1/DisClose_2013.pdf http://irep.iium.edu.my/51446/4/51446-DisClose_Discovering_Colossal_Closed_Itemsets-SCOPUS.pdf
first_indexed	2023-09-18T21:12:49Z
last_indexed	2023-09-18T21:12:49Z
_version_	1777411366583271424

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

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