Homogeneous Relational Data Clustering
Bo Long, Zhongfei Zhang, Philip S. Yu · 2010
In Chapter 4, we proposed a general model based on the graph approximation to learn relation-pattern-based cluster structures from a graph. The model generalizes the traditional graph partitioning approaches and is applicable to learning the various cluster structures. In this chapter, under the model we derive a family of algorithms which are flexible to learn various cluster structures and easy to incorporate the prior knowledge of the cluster structures. Specifically, we derive algorithms for the basic CLGA model in Definition 4.1 and its extensions, soft CLGA model and balanced CLGA model.