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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/51446/1/DisClose_2013.pdf
http://irep.iium.edu.my/51446/4/51446-DisClose_Discovering_Colossal_Closed_Itemsets-SCOPUS.pdf
http://irep.iium.edu.my/51446/
http://www.springer.com/us/book/9783642367779#
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spelling my.iium.irep.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 REM 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
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
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/51446/1/DisClose_2013.pdf
http://irep.iium.edu.my/51446/4/51446-DisClose_Discovering_Colossal_Closed_Itemsets-SCOPUS.pdf
http://irep.iium.edu.my/51446/
http://www.springer.com/us/book/9783642367779#
_version_ 1643613954457468928
score 13.211869

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