APPECT: an Approximate Backbone-Based Clustering Algorithm for Tags

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Zong, Yu, Xu, Guandong, Jin, Ping, Zhang, Yanchun, Chen, Enhong and Pan, Rong (2011) APPECT: an Approximate Backbone-Based Clustering Algorithm for Tags. In: Advanced data mining and applications : 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I. Tang, Jie, King, Irwin, Chen, Ling and Wang, Jianyong, eds. Lecture notes in artificial intelligence (7120). Springer, Berlin, pp. 175-189.

Abstract

In social annotation systems, users label digital resources by using tags which are freely chosen textual descriptions. Tags are used to index, annotate and retrieve resource as an additional metadata of resource. Poor retrieval performance remains a major problem of most social tagging systems resulting from the severe difficulty of ambiguity, redundancy and less semantic nature of tags. Clustering method is a useful tool to address the aforementioned difficulties. Most of the researches on tag clustering are directly using traditional clustering algorithms such as K-means or Hierarchical Agglomerative Clustering on tagging data, which possess the inherent drawbacks, such as the sensitivity of initialization. In this paper, we instead make use of the approximate backbone of tag clustering results to find out better tag clusters. In particular, we propose an APProximate backbonE-based Clustering algorithm for Tags (APPECT).The main steps of APPECT are: (1) we execute the K-means algorithm on a tag similarity matrix for M times and collect a set of tag clustering results Z = C 1,C 2,...,C m ; (2) we form the approximate backbone of Z by executing a greedy search; (3) we fix the approximate backbone as the initial tag clustering result and then assign the rest tags into the corresponding clusters based on the similarity. Experimental results on three real world datasets namely MedWorm, MovieLens and Dmoz demonstrate the effectiveness and the superiority of the proposed method against the traditional approaches.

Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/9619
Official URL http://link.springer.com/chapter/10.1007%2F978-3-6...
ISBN 9783642258527 (print), 9783642258534 (online)
Subjects Historical > Faculty/School/Research Centre/Department > Centre for Applied Informatics
Current > FOR Classification > 0806 Information Systems
Historical > SEO Classification > 8902 Computer Software and Services
Keywords ResPubID23705, tag clustering, algorithms, social annotation systems, social networking, online networks, Web 2.0 technologies
Citations in Scopus 3 - View on Scopus
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