Mining frequent sequences using itemset-based extension

In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we pre...

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Bibliographic Details
Main Authors:	Ma, Zhixin, Xu, Yusheng, Dillon, Tharam S., Chen, Xiaoyun
Other Authors:	Craig Douglas and Ping-Kong Alexander Wai
Format:	Conference Paper
Published:	IAENG 2008
Subjects:	data mining algorithms Frequent sequence mining frequent pattern sequence database
Online Access:	http://hdl.handle.net/20.500.11937/9047

id	curtin-20.500.11937-9047
recordtype	eprints
spelling	curtin-20.500.11937-90472017-01-30T11:10:17Z Mining frequent sequences using itemset-based extension Ma, Zhixin Xu, Yusheng Dillon, Tharam S. Chen, Xiaoyun Craig Douglas and Ping-Kong Alexander Wai data mining algorithms Frequent sequence mining frequent pattern sequence database In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we present FINDER, a novel algorithm for discovering the set of all frequent sequences. FINDER is composed oftwo separated steps. In the first step, all frequent itemsets are discovered and we can get great benefit from existing efficient itemset mining algorithms. In the second step, all frequent sequcnces with at least two frequent itemsets are detected by combining depth-first search and item set-based extension candidate generation together. A vertical bitmap data representation is adopted for rapidly support counting reason. Several pruning strategies are used to reduce the search space and minimize cost of computation. An extensive set ofexperiments demonstrate the effectiveness and the linear scalability of proposed algorithm. 2008 Conference Paper http://hdl.handle.net/20.500.11937/9047 IAENG fulltext
repository_type	Digital Repository
institution_category	Local University
institution	Curtin University Malaysia
building	Curtin Institutional Repository
collection	Online Access
topic	data mining algorithms Frequent sequence mining frequent pattern sequence database
spellingShingle	data mining algorithms Frequent sequence mining frequent pattern sequence database Ma, Zhixin Xu, Yusheng Dillon, Tharam S. Chen, Xiaoyun Mining frequent sequences using itemset-based extension
description	In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we present FINDER, a novel algorithm for discovering the set of all frequent sequences. FINDER is composed oftwo separated steps. In the first step, all frequent itemsets are discovered and we can get great benefit from existing efficient itemset mining algorithms. In the second step, all frequent sequcnces with at least two frequent itemsets are detected by combining depth-first search and item set-based extension candidate generation together. A vertical bitmap data representation is adopted for rapidly support counting reason. Several pruning strategies are used to reduce the search space and minimize cost of computation. An extensive set ofexperiments demonstrate the effectiveness and the linear scalability of proposed algorithm.
author2	Craig Douglas and Ping-Kong Alexander Wai
author_facet	Craig Douglas and Ping-Kong Alexander Wai Ma, Zhixin Xu, Yusheng Dillon, Tharam S. Chen, Xiaoyun
format	Conference Paper
author	Ma, Zhixin Xu, Yusheng Dillon, Tharam S. Chen, Xiaoyun
author_sort	Ma, Zhixin
title	Mining frequent sequences using itemset-based extension
title_short	Mining frequent sequences using itemset-based extension
title_full	Mining frequent sequences using itemset-based extension
title_fullStr	Mining frequent sequences using itemset-based extension
title_full_unstemmed	Mining frequent sequences using itemset-based extension
title_sort	mining frequent sequences using itemset-based extension
publisher	IAENG
publishDate	2008
url	http://hdl.handle.net/20.500.11937/9047
first_indexed	2018-09-06T18:31:20Z
last_indexed	2018-09-06T18:31:20Z
_version_	1610883982332788736

Mining frequent sequences using itemset-based extension

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