Abstract
Along with the development of Web2.0, folksonomy has become a hot topic related to data mining, information retrieval and social network. The tag semantic is the key for deep understanding the correlation of objects in folksonomy. This paper proposes two methods to cluster tags for core-tag by fusing multi-similarity measurements. The contributions of this paper include: (1) Proposing the concept of core-tag and the model of core-tag clusters. (2) Designing a core-tag clustering algorithm CETClustering, based on clustering ensemble method. (3) Designing a second kind of core-tag clustering algorithm named SkyTagClustering, based on skyline operator. (4) Comparing the two algorithms with modified K-means. Experiments show that the two algorithms are better than modified K-means with 20-30% on efficiency and 20% higher scores on quality.
Supported by the 11th Five Years Key Programs for Sci. & Tech. Development of China under grant No. 2006BAI05A01, the National Science Foundation under grant No. 60773169, the Software Innovation Project of Sichuan Youth under grant No. 2007AA0155.
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Jiang, Y. et al. (2009). Core-Tag Clustering for Web 2.0 Based on Multi-similarity Measurements. In: Chen, L., et al. Advances in Web and Network Technologies, and Information Management. APWeb WAIM 2009 2009. Lecture Notes in Computer Science, vol 5731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03996-6_21
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DOI: https://doi.org/10.1007/978-3-642-03996-6_21
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