BOSTER : an efficient algorithm for mining frequent unordered induced subtrees

Chowdhury, Israt J. & Nayak, Richi (2014) BOSTER : an efficient algorithm for mining frequent unordered induced subtrees. Lecture Notes in Computer Science : Web Information Systems Engineering – WISE 2014, 8786, pp. 146-155.

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Abstract

Extracting frequent subtrees from the tree structured data has important applications in Web mining. In this paper, we introduce a novel canonical form for rooted labelled unordered trees called the balanced-optimal-search canonical form (BOCF) that can handle the isomorphism problem efficiently. Using BOCF, we define a tree structure guided scheme based enumeration approach that systematically enumerates only the valid subtrees. Finally, we present the balanced optimal search tree miner (BOSTER) algorithm based on BOCF and the proposed enumeration approach, for finding frequent induced subtrees from a database of labelled rooted unordered trees. Experiments on the real datasets compare the efficiency of BOSTER over the two state-of-the-art algorithms for mining induced unordered subtrees, HybridTreeMiner and UNI3. The results are encouraging.

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2 citations in Scopus
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1 citations in Web of Science®

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ID Code: 78881
Item Type: Journal Article
Refereed: Yes
Additional Information: 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, Proceedings, Part I
Keywords: Web mining, Frequent subtrees, Labelled rooted unordered trees, Induced subtrees, Canonical form, Enumeration approach
DOI: 10.1007/978-3-319-11749-2_12
ISSN: 0302-9743
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Springer International Publishing Switzerland
Copyright Statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11749-2_12
Deposited On: 24 Nov 2014 00:01
Last Modified: 29 Nov 2014 03:57

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