We propose a novel parallel implementation of the fuzzy clustering algorithm.We redefine a fuzzy clustering technique to improve data-parallelism.Our method enhances the execution time for the classification of large data-sets. Clustering aims to classify different patterns into groups called clusters. Many algorithms for both hard and fuzzy clustering have been developed to deal with exploratory data analysis in many contexts such as image processing, pattern recognition, etc. However, we are witnessing the era of big data computing where computing resources are becoming the main bottleneck to deal with those large datasets. In this context, sequential algorithms need to be redesigned and even rethought to fully leverage the emergent massively parallel architectures. In this paper, we propose a parallel implementation of the fuzzy minimals clustering algorithm called Parallel Fuzzy Minimal (PFM). Our experimental results reveal linear speed-up of PFM when compared to the sequential counterpart version, keeping very good classification quality.
['Isabel Vizcaíno Timón', 'Jesús Soto', 'Horacio Pérez-Sánchez', 'José M. Cecilia']
Parallel implementation of fuzzy minimals clustering algorithm