DENCLUE-DE: Differential Evolution Based DENCLUE for Scalable Clustering in Big Data Analysis

In data analysis, clustering is one of the important tasks. In this context many clustering methods are proposed in literature for big data analysis. Density based clustering (DENCLUE) is one of the powerful unsupervised clustering methods for the huge volume of data sets. In denclue, hill climbing plays important role to find the density attractor. In this paper, we apply Differential evolutionary algorithm in the place of hill climbing to find the global optimum solution. In this model, we propose Gaussian based mutation function in DE to improve the accuracy and execution time on spark platform. We test this approach on big data sets presented in literature. Experimental results shows that the proposed approach outperforms other variants in terms of execution time.

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