CLAP: Collaborative pattern mining for distributed information systems

Zhu, X; Li, B; Wu, X; He, D; Zhang, C

CLAP: Collaborative pattern mining for distributed information systems

Zhu, X Li, B Wu, X He, D Zhang, C

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Publication Type:: Journal Article
Citation:: Decision Support Systems, 2011, 52 (1), pp. 40 - 51
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Li, B	en_US
dc.contributor.author	Wu, X	en_US
dc.contributor.author	He, D	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Decision Support Systems, 2011, 52 (1), pp. 40 - 51	en_US
dc.identifier.issn	0167-9236	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18233
dc.description.abstract	The purpose of data mining from distributed information systems is usually threefold: (1) identifying locally significant patterns in individual databases; (2) discovering emerging significant patterns after unifying distributed databases in a single view; and (3) finding patterns which follow special relationships across different data collections. While existing research has significantly advanced the techniques for mining local and global patterns (the first two goals), very little attempt has been made to discover patterns across distributed databases (the third goal). Moreover, no framework currently exists to support the mining of all three types of patterns. This paper proposes solutions to discover patterns from distributed databases. More specifically, we consider pattern mining as a query process where the purpose is to discover patterns from distributed databases with patterns' relationships satisfying user specified query constraints. We argue that existing self-contained mining frameworks are neither efficient, nor feasible to fulfill the objective, mainly because their pattern pruning is single-database oriented. To solve the problem, we advocate a cross-database pruning concept and propose a collaborative pattern (CLAP) mining framework with cross-database pruning mechanisms for distributed pattern mining. In CLAP, distributed databases collaboratively exchange pattern information between sites so that each site can leverage information from other sites to gain cross-database pruning. Experimental results show that CLAP fits a niche position, and demonstrate that CLAP not only outperforms its other peers with significant runtime performance gains, but also helps find patterns incapable of being discovered by others. © 2011 Published by Elsevier B.V. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP1093762
dc.relation.ispartof	Decision Support Systems	en_US
dc.relation.isbasedon	10.1016/j.dss.2011.05.002	en_US
dc.subject.classification	Information Systems	en_US
dc.title	CLAP: Collaborative pattern mining for distributed information systems	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	52	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	52	en_US

Abstract:

The purpose of data mining from distributed information systems is usually threefold: (1) identifying locally significant patterns in individual databases; (2) discovering emerging significant patterns after unifying distributed databases in a single view; and (3) finding patterns which follow special relationships across different data collections. While existing research has significantly advanced the techniques for mining local and global patterns (the first two goals), very little attempt has been made to discover patterns across distributed databases (the third goal). Moreover, no framework currently exists to support the mining of all three types of patterns. This paper proposes solutions to discover patterns from distributed databases. More specifically, we consider pattern mining as a query process where the purpose is to discover patterns from distributed databases with patterns' relationships satisfying user specified query constraints. We argue that existing self-contained mining frameworks are neither efficient, nor feasible to fulfill the objective, mainly because their pattern pruning is single-database oriented. To solve the problem, we advocate a cross-database pruning concept and propose a collaborative pattern (CLAP) mining framework with cross-database pruning mechanisms for distributed pattern mining. In CLAP, distributed databases collaboratively exchange pattern information between sites so that each site can leverage information from other sites to gain cross-database pruning. Experimental results show that CLAP fits a niche position, and demonstrate that CLAP not only outperforms its other peers with significant runtime performance gains, but also helps find patterns incapable of being discovered by others. © 2011 Published by Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/18233