| Summary: | 碩士 === 中原大學 === 資訊工程研究所 === 100 === Social tagging systems allow people to attach tags to data. Studies and applications surrounding the tags are thus prevalently developed. Due to the diversity of tags and the difference of individual perception, people often fail to use proper terms as tags and systems also fail to collect a wide range of tagging information. Consequently, the tag-based classification and retrieval services cannot be forwarded. To enhance the usage of tags, effective tag suggestion, also the motivation of this study has become one of the goals pursued by many systems. In recent years, many methods for searching or filtering tags have been proposed in the literature. We further studied how to organize the large number of tags to quickly build a tree structure composed of hierarchical tag clustering, which would help the system build a progressive tags suggestion mechanism. Our approach takes in account both the importance of individual tags and the similarity among tags. Using the techniques of clustering and outlier detection, it can compute the tag clustering at each layer according to the given number of clusters. For performance evaluation of our method, we made experiments on the tagging data obtained from the Web. Through a series of self-defined measurement indicators, such as the intra-cluster similarity and the inter-cluster dissimilarity, we analyzed the tree structure built by our method. More than half of the results showed that our approach achieved the degree of excellence under at least two of the indicators.
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