Fast multidimensional clustering of categorical data
Tengfei Liu, Nevin Lianwen Zhang, Leonard K. M. Poon, Yi Wang, Liu Hua · 2011
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 (LTMs). This method has a number of advantages over alternative methods, but is computationally inefficient. We propose a fast algorithm for learning LTMs and show that the algorithm can produce rich and meaningful clustering results in moderately large data sets.