A novel LTM-based method for multi-partition clustering

Abstract

Early research work on clustering usually assumed that there was one true clustering of data. However, complex data are typically multifaceted and can be meaningfully clustered in many different ways. There is a growing interest in methods that produce multiple partitions of data. One such method is based on latent tree models (LTM). But previous methods for learning general LTM are computationally inefficient. In this paper, we propose a fast algorithm for learning LTM. Empirical results on two real world datasets are given to show that our method can produce rich and meaningful partitions.