Bringing order to the Web: automatically categorizing search results
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Tag-based social interest discovery
Proceedings of the 17th international conference on World Wide Web
Co-Clustering Tags and Social Data Sources
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Extracting data records from the web using tag path clustering
Proceedings of the 18th international conference on World wide web
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Document recommendation in social tagging services
Proceedings of the 19th international conference on World wide web
Searching and Browsing Tag Spaces Using the Semantic Tag Clustering Search Framework
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
APPECT: an approximate backbone-based clustering algorithm for tags
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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Under social tagging systems, a typical Web2.0 application, users label digital data sources 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 increase the ability of information retrieval in the aforementioned systems. In this paper, we propose a novel clustering algorithm named LIPC (Local Information Passing Clustering algorithm). The main steps of LIPC are: (1) we estimate a KNN neighbor directed graph G of tags and calculate the kernel density of each tag in its neighborhood; (2) we generate local information, local coverage and local kernel of each tag; (3) we pass the local information on G by I and O operators until they are converged and tag priory are generated; (4) we use tag priory to find out the clusters of tags. Experimental results on two real world datasets namely MedWorm and MovieLens demonstrate the efficiency and the superiority of the proposed method.