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A Weighted Partitioning Dynamic Clustering Algorithm for Quantitative Feature Data Based on Adaptive Euclidean Distances

This paper introduces a weighted partitioning dynamic clustering algorithm for quantitative feature data based on adaptive euclidean distances. The proposed method is an iterative four-steps relocation algorithm involving the determination of the clusters representatives (prototypes), the weight of each individual, the distance associated to each cluster and the construction of the clusters, at each iteration. Moreover, the algorithm furnishes automatically the best weight of each individual in such a way that as close it is an individual from the prototype of the cluster it belongs as high it is its weight. Experiments with real and synthetic datasets show the usefulness of the proposed method.