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One of the most popular clustering techniques is the k-means clustering algorithm. However, the utilization of the k-means is severely limited by its high computational complexity. In this study, we propose a new strategy to accelerate the k-means clustering algorithm through the Partial Distance (PD) logic. The proposed strategy avoids many unnecessary distance calculations by applying efficient PD strategy. Experiments show the efficiency of the proposed strategy when applied to different data sets. Clustering, k-means algorithm, pattern recognition, partial distance, INTRODUCTION Clustering techniques have become very popular in a number of areas, such as engineering, medicine, biology and data mining (1), (2). A good survey on clustering algorithms can be [...]

We consider the problem of variable or feature selection for model-based clustering. The problem of comparing two nested subsets of variables is recast as a model comparison problem and addressed using approximate Bayes factors. A greedy search algorithm is proposed for finding a local optimum in model space. The resulting method selects variables (or features), the number of clusters, and the clustering model simultaneously. We applied the method to several simulated and real examples and found that removing irrelevant variables often improved performance. Compared with methods based on all of the variables, our variable selection method consistently yielded more accurate estimates of the number of groups and lower classification error rates, as well as more parsimonious clustering models and easier visualization of results. KEY WORDS: Bayes factor; BIC; Feature selection; Model-based clustering; Unsupervised learning; Variable selection.

Over the last decade, technological advances have generated an explosion of data with substantially smaller sample size relative to the number of covariates (p [much greater than] n). A common goal in the analysis of such data involves uncovering the group structure of the observations and identifying the discriminating variables. In this article we propose a methodology for addressing these problems simultaneously. Given a set of variables, we formulate the clustering problem in terms of a multivariate normal mixture model with an unknown number of components and use the reversible-jump Markov chain Monte Carlo technique to define a sampler that moves between different dimensional spaces. We handle the problem of selecting a few predictors among the prohibitively vast number of variable subsets by introducing a binary exclusion/inclusion latent vector, which gets updated via stochastic search techniques. We specify conjugate priors and exploit the conjugacy by integrating out some of the parameters. We describe strategies for posterior inference and explore the performance of the methodology with simulated and real datasets. KEY WORDS: Bayesian variable selection; Bayesian clustering; Label switching; Reversible-jump Markov chain Monte Carlo.

1. OVERVIEW It is truly a delight for us to read the inspirational work by Morris, Vannucci, Brown, and Carroll (abbreviated as MVBC hereafter). To the best of our knowledge, [...]

Byline: Elisabeth T. Pereira, Antonio Jorge Fernandes This study examines the cluster concept and the importance of developing clusters to obtain and sustain competitive advantages, either as a regional competitive factor or to the level of industrial competitiveness.