APPECT: An Approximate Backbone-Based Clustering Algorithm for Tags

Publisher:
Springer Berlin / Heidelberg
Publication Type:
Conference Proceeding
Citation:
Advanced Data Mining and Applications, Lecture Notes in Computer Science, 2011, 7120 pp. 175 - 189
Issue Date:
2011-01
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In social annotation systems, users label digital resources by using tags which are freely chosen textual descriptions. Tags are used to index, anno- tate 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 difficul- ties. Most of the researches on tag cluste ring are directly using traditional clus- tering 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.
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