Solution strategy based on Gaussian mixture models and dispersion reduction for the capacitated centered clustering problem

Clustering search algorithm for the capacitated centered clustering problem

The Capacitated Centred Clustering Problem (CCCP) consists in partitioning a set of n points into p disjoint clusters with a known capacity. Each cluster is specified by a centroid. The objective is to minimize the total dissimilarity within each cluster, such that a given capacity limit of the cluster is not exceeded. This paper presents a solution procedure for the CCCP, using the hybrid meta...

A Hybrid Approach for Optimization of Capacitated Centered Clustering Problem

Capacitated Centered Clustering problem (CPMP) is an important variation of p-median problems. The p-median problems are known to be NP-hard. A hybrid genetic algorithm is used to solve the problem efficiently. The analysis shows that the performance of the proposed algorithm is better than the traditional local search heuristic.

Mixture models for clustering and dimension reduction

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Hard-Clustering with Gaussian Mixture Models

Training the parameters of statistical models to describe a given data set is a central task in the field of data mining and machine learning. A very popular and powerful way of parameter estimation is the method of maximum likelihood estimation (MLE). Among the most widely used families of statistical models are mixture models, especially, mixtures of Gaussian distributions. A popular hard-clu...

Randomized heuristics for the Capacitated Clustering Problem