Optimal Tabu K-Means Clustering Using Massively Parallel Architecture

Abstract

Parallel discovery of inherent clusters using massively threaded architectures is the solution for handling computational challenges raised by fat datasets in cluster analysis. The Graphics Processing Unit and Compute Unified Device Architecture form a convincing platform to parallelize clustering algorithms. The parallel K-means algorithm aims at increasing the speedup, but often faces the hitch of falling into local minima. The heuristic search procedure to discover the global optima in the solution space is known as Tabu Search. The K-means clustering solution is fine-tuned by applying parallel implementation of Tabu Search K-means clustering in order to increase efficacy. The aim is to combine optimization characteristic of Tabu Search for calculation of centroids with clustering attitude of K-means and to enhance the solution using processing power of GPU. The parallelization strategy used exhibits increase in speedup. The parallel Tabu-KM algorithm is tested on standard datasets, and performance is compared with sequential K-means, parallel K-means and sequential Tabu K-means algorithms. The experimental results confirm yet another parallelization technique to unravel data clustering problems.