A novel method for finding similarities between unordered trees using matrix data model

Chowdhury, Israt J. & Nayak, Richi (2013) A novel method for finding similarities between unordered trees using matrix data model. Lecture Notes in Computer Science, 8180, pp. 421-430.

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Abstract

Trees are capable of portraying the semi-structured data which is common in web domain. Finding similarities between trees is mandatory for several applications that deal with semi-structured data. Existing similarity methods examine a pair of trees by comparing through nodes and paths of two trees, and find the similarity between them. However, these methods provide unfavorable results for unordered tree data and result in yielding NP-hard or MAX-SNP hard complexity. In this paper, we present a novel method that encodes a tree with an optimal traversing approach first, and then, utilizes it to model the tree with its equivalent matrix representation for finding similarity between unordered trees efficiently. Empirical analysis shows that the proposed method is able to achieve high accuracy even on the large data sets.

Impact and interest:

3 citations in Scopus
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3 citations in Web of Science®

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72 since deposited on 27 Nov 2013
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ID Code: 64797
Item Type: Journal Article
Refereed: Yes
Additional Information: Web Information Systems Engineering – WISE 2013 : 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part I.
Keywords: Semi-structured Data, Unordered Tree, Similarity Measure, Matrix Representation
DOI: 10.1007/978-3-642-41230-1_35
ISBN: 9783642412301
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)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Springer-Verlag Berlin Heidelberg
Copyright Statement: The final publication is available at link.springer.com
Deposited On: 27 Nov 2013 00:12
Last Modified: 03 Nov 2014 06:35

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