Machine learning approaches to link-based clustering

Abstract

We have reviewed several state-of-the-art machine learning approaches to different types of link-based clustering in this chapter. Specifically, we have presented the spectral clustering for heterogeneous relational data, the symmetric convex coding for homogeneous relational data, the citation model for clustering the special but popular homogeneous relational data-the textual documents with citations, the probabilistic clustering framework on mixed membership for general relational data, and the statistical graphical model for dynamic relational clustering. We have demonstrated the effectiveness of these machine learning approaches through empirical evaluations.