JCSE, vol. 10, no. 1, pp.1-8, 2016

DOI: http://dx.doi.org/10.5626/JCSE.2016.10.1.1

An Improved Hybrid Canopy-Fuzzy C-Means Clustering Algorithm Based on MapReduce Model

Wei Dai, Changjun Yu and Zilong Jiang
*School of Economics and Management, Hubei Polytechnic University, Huangshi, China School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China

Abstract: The fuzzy c-means (FCM) is a frequently utilized algorithm at present. Yet, the clustering quality and convergence rate of FCM are determined by the initial cluster centers, and so an improved FCM algorithm based on canopy cluster concept to quickly analyze the dataset has been proposed. Taking advantage of the canopy algorithm for its rapid acquisition of cluster centers, this algorithm regards the cluster results of canopy as the input. In this way, the convergence rate of the FCM algorithm is accelerated. Meanwhile, the MapReduce scheme of the proposed FCM algorithm is designed in a cloud environment. Experimental results demonstrate the hybrid canopy-FCM clustering algorithm processed by MapReduce be endowed with better clustering quality and higher operation speed.

Keyword: FCM; Canopy; Clustering; MapReduce

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