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Efficient mining of top-k breaker emerging subgraph patterns from graph datasets

Gan, Min and Dai, Honghua 2009, Efficient mining of top-k breaker emerging subgraph patterns from graph datasets, in AusDM 09 : Conferences in Research and Practice in Information Technology Series, Australian Computer Society, Crows Nest, N.S.W., pp. 183-191.

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Title Efficient mining of top-k breaker emerging subgraph patterns from graph datasets
Author(s) Gan, Min
Dai, Honghua
Conference name Australasian Data Mining. Conference (8th : 2009 : Melbourne, Vic.)
Conference location Melbourne, Vic.
Conference dates 1-4 Dec. 2009
Title of proceedings AusDM 09 : Conferences in Research and Practice in Information Technology Series
Publication date 2009
Start page 183
End page 191
Total pages 9
Publisher Australian Computer Society
Place of publication Crows Nest, N.S.W.
Keyword(s) Breaker emerging subgraph patterns
Discriminative patterns
Graph mining
Summary This paper introduces a new type of discriminative subgraph pattern called breaker emerging subgraph pattern by introducing three constraints and two new concepts: base and breaker. A breaker emerging sub-graph pattern consists of three subpatterns: a con-strained emerging subgraph pattern, a set of bases and a set of breakers. An efficient approach is pro-posed for the discovery of top-k breaker emerging sub-graph patterns from graph datasets. Experimental re-sults show that the approach is capable of efficiently discovering top-k breaker emerging subgraph patterns from given datasets, is more efficient than two previ-ous methods for mining discriminative subgraph pat-terns. The discovered top-k breaker emerging sub-graph patterns are more informative, more discrim-inative, more accurate and more compact than the minimal distinguishing subgraph patterns. The top-k breaker emerging patterns are more useful for sub-structure analysis, such as molecular fragment analy-sis. © 2009, Australian Computer Society, Inc.
ISBN 9781920682828
ISSN 1445-1336
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2009, Australian Computer Society
Persistent URL http://hdl.handle.net/10536/DRO/DU:30067588

Document type: Conference Paper
Collections: School of Information Technology
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