A General Model for Relational Clustering
Bo Long, Zhongfei Mark · InTech eBooks · 2011
Relational learning has attracted more and more attention in recent years due to its phenomenal impact in various important applications which involve multi-type interrelated data objects, such as bioinformatics, citation analysis, epidemiology and web analytics. However, the research on unsupervised relational learning is still limited and preliminary. In this paper, we propose a general model, the collective factorization on related matrices, for multi-type relational data clustering. The model is applicable to relational data with various structures. Under the proposed model with a specific distance function – Euclidean distance function, we derive a novel spectral clustering algorithm, spectral relational clustering, to cluster multi-type interrelated data objects simultaneously. The algorithm iteratively embeds each type of data objects into low dimensional spaces and benefits from the interactions among the hidden structures of different types of data objects. Extensive experiments demonstrate the promise and effectiveness of the proposed model and algorithm. 1.