Sharon M. McNicholas | Searcharxiv

arXiv2020

An Evolutionary Algorithm with Crossover and Mutation for Model-Based Clustering

An evolutionary algorithm (EA) is developed as an alternative to the EM algorithm for parameter estimation in model-based clustering. This EA facilitates a different search of the fitness landscape, i.e., the likelihood surface, utilizing both crossover and mutation. Furthermore, this EA represents an efficient approach to "hard" model-based clustering and so it can be viewed as a sort of generalization of the k-means algorithm, which is itself equivalent to a restricted Gaussian mixture model. The EA is illustrated on several datasets, and its performance is compared to other hard clustering approaches and model-based clustering via the EM algorithm.

stat.CO↗

arXiv2014

Mixtures of Variance-Gamma Distributions

A mixture of variance-gamma distributions is introduced and developed for model-based clustering and classification. The latest in a growing line of non-Gaussian mixture approaches to clustering and classification, the proposed mixture of variance-gamma distributions is a special case of the recently developed mixture of generalized hyperbolic distributions, and a restriction is required to ensure identifiability. Our mixture of variance-gamma distributions is perhaps the most useful such special case and, we will contend, may be more useful than the mixture of generalized hyperbolic distributions in some cases. In addition to being an alternative to the mixture of generalized hyperbolic distributions, our mixture of variance-gamma distributions serves as an alternative to the ubiquitous mixture of Gaussian distributions, which is a special case, as well as several non-Gaussian approaches, some of which are special cases. The mathematical development of our mixture of variance-gamma distributions model relies on its relationship with the generalized inverse Gaussian distribution; accordingly, the latter is reviewed before our mixture of variance-gamma distributions is presented. Parameter estimation carried out within the expectation-maximization framework.

stat.ME↗