Non-parametric mixture models for clustering

Abstract

Mixture models have been widely used for data clustering. However, commonly used mixture models are generally of a parametric form (e.g., mixture of Gaussian distributions or GMM), which significantly limits their capacity in fitting diverse multidimensional data distributions encountered in practice. We propose a non-parametric mixture model (NMM) for data clustering in order to detect clusters generated from arbitrary unknown distributions, using non-parametric kernel density estimates. The proposed model is non-parametric since the generative distribution of each data point depends only on the rest of the data points and the chosen kernel. A leave-one-out likelihood maximization is performed to estimate the parameters of the model. The NMM approach, when applied to cluster high dimensional text datasets significantly outperforms the state-of-the-art and classical approaches such as K-means, Gaussian Mixture Models, spectral clustering and linkage methods. © 2010 Springer-Verlag Berlin Heidelberg.