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Multi-View Clustering of Social-Based Data
Real-world, social phenomena produce various types of data, like explicit networks or user-emitted text. When different sets of data describe the same entities, the data is termed multi-view or multi-modal. A distinct advantage of multi-view data is that different views may better capture different aspects of the latent structure of the data. However, there are difficulties in combining that data to produce somethinglike a clustering of the data. Multi-view clustering techniques, primarily developed for image or biological use cases or network only use cases, have typically not been used for clustering social-based use cases. I investigate the use of multi-view clustering on various social-based, multi-view data sets, and propose new techniques for multi-view clustering of social-based data.
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