Relational clustering model provides a principal framework to unify various tasks including traditional attributes-based clustering, semi-supervised clustering and co-clustering


This model seeks to identify cluster structures for each type of data objects and interaction patterns between different types of objects. It is applicable to relational data of various structures. Under this model, we propose parametric hard and soft relational clustering algorithms under a large number of exponential family distributions.

Key Benefits

The invention is applicable to various relational data from various applications. It is capable of adapting different distribution assumptions for different relational data with different statistical properties. The resulting parameter matrices provides an intuitive summary for the hidden structure for the relational data.

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