Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Modern Information Retrieval
Usage patterns of collaborative tagging systems
Journal of Information Science
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
Integrating Folksonomies with the Semantic Web
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Co-Clustering Tags and Social Data Sources
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Applications of web query mining
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Clustering the tagged resources using STAC
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
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
ACM Transactions on Management Information Systems (TMIS)
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Nowadays, the most dominant and noteworthy web information sources are developed according to the collaborative-web paradigm, also known as Web 2.0. In particular, it represents a novel paradigm in the way users interact with the web. Users (also called prosumers) are no longer passive consumers of published content, but become involved, implicitly and explicitly, as they cooperate by providing their own resources in an "architecture of participation". In this scenario, collaborative tagging, i.e., the process of classifying shared resources by using keywords, becomes more and more popular. The main problem in such task is related to well-known linguistic phenomena, such as polysemy and synonymy, making effective content retrieval harder. In this paper, an approach that monitors users activity in a tagging system and dynamically quantifies associations among tags is presented. The associations are then used to create tags clusters. Experiments are performed comparing the proposed approach with a state-of-the-art tag clustering technique. Results ---given in terms of classical precision and recall--- show that the approach is quite effective in the presence of strongly related tags in a cluster.