Pattern space maintenance for data updates and interactive mining

Feng, M; Dong, G; Li, J; Tan, YP; Wong, L

Pattern space maintenance for data updates and interactive mining

Feng, M Dong, G Li, J

Tan, YP Wong, L

Permalink

Publication Type:: Journal Article
Citation:: Computational Intelligence, 2010, 26 (3), pp. 282 - 317
Issue Date:: 2010-08-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (441.86 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Feng, M	en_US
dc.contributor.author	Dong, G	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0003-1833-7413	en_US
dc.contributor.author	Tan, YP	en_US
dc.contributor.author	Wong, L https://orcid.org/0000-0003-1241-5441	en_US
dc.date.issued	2010-08-01	en_US
dc.identifier.citation	Computational Intelligence, 2010, 26 (3), pp. 282 - 317	en_US
dc.identifier.issn	0824-7935	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15136
dc.description.abstract	This article addresses the incremental and decremental maintenance of the frequent pattern space. We conduct an in-depth investigation on how the frequent pattern space evolves under both incremental and decremental updates. Based on the evolution analysis, a new data structure, Generator-Enumeration Tree (GE-tree), is developed to facilitate the maintenance of the frequent pattern space. With the concept of GE-tree, we propose two novel algorithms, Pattern Space Maintainer+ (PSM+) and Pattern Space Maintainer- (PSM-), for the incremental and decremental maintenance of frequent patterns. Experimental results demonstrate that the proposed algorithms, on average, outperform the representative state-of-the-art methods by an order of magnitude. © 2010 Wiley Periodicals, Inc.	en_US
dc.relation.ispartof	Computational Intelligence	en_US
dc.relation.isbasedon	10.1111/j.1467-8640.2010.00360.x	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Pattern space maintenance for data updates and interactive mining	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	26	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
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 - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

This article addresses the incremental and decremental maintenance of the frequent pattern space. We conduct an in-depth investigation on how the frequent pattern space evolves under both incremental and decremental updates. Based on the evolution analysis, a new data structure, Generator-Enumeration Tree (GE-tree), is developed to facilitate the maintenance of the frequent pattern space. With the concept of GE-tree, we propose two novel algorithms, Pattern Space Maintainer+ (PSM+) and Pattern Space Maintainer- (PSM-), for the incremental and decremental maintenance of frequent patterns. Experimental results demonstrate that the proposed algorithms, on average, outperform the representative state-of-the-art methods by an order of magnitude. © 2010 Wiley Periodicals, Inc.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/15136