| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 103 === In the past decades, enormous research studies on clustering have been conducted, and many clustering algorithms have been applied to various application domains. However, most of them focus on improving the performance without defining the meanings of the clusters, since no labeled data can be used to infer the meanings of clusters. Some of the algorithms on documents clustering can use the word features, such as frequencies, probabilities, or topic models, to give the clusters appropriate tags. This technique fails to apply to the other domains. This thesis proposes a framework to cluster users according to their behaviors and automatically tag the clusters. The proposed framework comprises three stages: latent factor discovery, clustering, and tagging. In most application settings, the number of clusters is unavailable especially when the data size is very large. This thesis proposes to use nonparametric clustering algorithms in the second stage, and DDCRP is used in the experiments. The output of the framework is clusters, each of which is associated with tags. We conduct experiments on three data sets and compare with several algorithms, and evaluate with clustering performance and tag accuracy. The experimental results indicate that the proposed approach works well and outperforms other algorithms in most experiments.
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