並列摘要
With the popularity of Internet, the World Wide Web contains a giant amount of information. To search relevant information from large number of texts becomes a challenge to the users. Hierarchical clustering is one of the methods to conquer this problem. Because its features let users can browse the topic gradually and find out the most relevant documents they have interesting. But there are still have some challenge in hierarchical clustering must be addressed, like high dimensionality of the data, dynamic data sets, the sensitivity of input order, documents has several concept, and the relationship of clusters and tags. In this paper, we propose an approach of dynamic hierarchical clustering based on taxonomy to conquer those challenges. The experimental result shows that our method not only suitable for constructing hierarchical clustering in dynamic data sets, but also offer a easier structure to browse than traditional algorithms, BKM and UPGMA. In addition, the clusters are labeled meaningful tags with the relationship of containment can let users understand the whole concept of clusters rapidly.