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The emergence of social networking web sites has created numerous interactive sharing platforms for users to upload, comment, and share multimedia content online within their social circles. It has led to the massive number of web multimedia documents, together with their rich meta-information, such as category information, user tagging and description, and user comments. Such interconnected but heterogeneous social media data has provided opportunities for understanding traditional multimedia data, such as images and text documents. More importantly, the different types of activities and interactions of social users could be utilized to understand and analyze user behaviors, and discover social trends in social networks. Clustering is an important approach to the analysis and mining of social media data. However, different from traditional multimedia data, the social media data are typically massive, diverse, heterogeneous and noisy. Those characteristics of social media data raise new challenges for existing clustering techniques, including the scalability to big data, the ability to automatically recognize the number of clusters in data sets, the strategies to effectively integrate data from heterogeneous resources for clustering, and the robustness to noisy features. Moreover, considering that different social users may have different preferences for categorizing the social media data, incorporating user preferences into the clustering framework to produce personalized data clusters is also a challenge. In order to address the above issues, in this thesis, we investigate and develop novel clustering algorithms for the fast and robust clustering of large-scale social media data by integrating their multiple but different types of features and user preferences, and explore their applications to the associative social media mining tasks. Towards this goal, we have completed four key tasks. First, we developed a twostep semi-supervised hierarchical clustering algorithm, termed Personalized Hierarchical

Clustering and heterogeneous information fusion for social media theme discovery and associative mining