Output details
11 - Computer Science and Informatics
University of Liverpool
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Output 0 of 0 in the submission
Article title
Efficient Phrase-Based Document Similarity for Clustering
Type
D - Journal article
Title of journal
IEEE Transactions on Knowledge and Data Engineering
Article number
-
Volume number
20
Issue number
9
First page of article
1217
ISSN of journal
1041-4347
Year of publication
2008
URL
-
Number of additional authors
1
Additional information
<17>This paper extends a WWW 2007 paper, which was not returned in RAE 2008. The similarity measure proposed in this work has been widely acknowledged as one of the canonical approaches for evaluation of clustering, among others, by Wang et al. (CIKM, 2011) and Fuhr et al. (Information Retrieval, 2011) in the context of information retrieval, and by Apeltsin et al. (Bioinformatics, 2011) and Novosad et al. (IEEE Transactions on Information Technology in Biomedicine, 2010) in applications of clustering to bioinformatics and biomedicine. Welch et al. (CIKM, 2010) used the proposed measurement method for discovering dominant terms in video retrieval.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
39
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
