III-COR-Small: Multi-Relational Data Clustering with Probabilistic Mixture Models

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With widespread attempts to apply data mining methods to real lifeproblems, there is an increasing realization that real life data isoften multi-relational, involving observations connecting multipleentities through a set of relations. A central problem in severalapplications involving multi-relational data is to simultaneously findclusters of objects across related entities, e.g., customer clustersand related product clusters in e-commerce, movie clusters and relateduser clusters in recommendation systems, communities and sharedcontent in social networks, etc. The key novel aspect is that theclustering of objects in an entity, such as the set of movies orusers, depends on its relationships with objects in other entities,e.g., users are similar if they like similar movies, and vice versa.The primary goal of of this project is to develop a unifiedstatistical approach to multi-relational clustering and relatedproblems in multi-relational data analysis. Towards this end, theproject investigates a family of novel statistical multi-relationalmixture models, with focus on additive and multiplicative models formulti-relational clustering. Due to modularity of design, bothadditive and multiplicative models can incorporate domain specificsemantics as well as automatic model selection using appropriateBayesian priors. Further, the project investigates efficientvariational inference methods appropriate for discovering latentmulti-relational clusters.The project significantly empowers the knowledge discovery componentof data mining. Crucial clues to understanding observed data inseveral disciplines, including social, biological, and informationsciences, are often spread across multiple related observations. Theproject enables a statistical approach to detecting latent structurein such multi-relational data, which is an important step towardsknowledge discovery from multiple related data sources. The projectplays an important role in developing closer collaboration acrossdisciplines and broaden participation in computer science. Buildingon the increasing awareness regarding the ubiquity of multi-relationaldata, the project contributes to the development of appropriateeducational material for the next generation work-force. Furtherinformation on the project may be found at the project web site:http://www.cs.umn.edu/~banerjee/multi-relational.