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MapReduce FCM clustering set algorithm

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Abstract

Fuzzy C-means clustering integration algorithm is a method to improve clustering quality by using integration ideas, but as the amount of data increases, its time complexity increases. A parallel FCM clustering integration algorithm based on MapReduce is proposed. The algorithm uses a random initial clustering centre to obtain differentiated cluster members. By establishing an overlapping matrix between clusters, the clustering labels are unified to find logical equivalence clusters. The cluster members share the classification information of the data objects by voting to obtain the final clustering result. The experimental results show that the parallel FCM clustering integration algorithm has good performance, and has high speedup and good scalability.

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Authors and Affiliations

  1. College of Computer Science and Technology, Beijing University of Technology, Beijing, 100124, China

    Mesmin J Mbyamm Kiki

  2. College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China

    Jianbiao Zhang & Bonzou Adolphe Kouassi

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  1. Mesmin J Mbyamm Kiki
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  2. Jianbiao Zhang
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  3. Bonzou Adolphe Kouassi
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Correspondence to Bonzou Adolphe Kouassi.

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Mbyamm Kiki, M.J., Zhang, J. & Kouassi, B.A. MapReduce FCM clustering set algorithm. Cluster Comput 24, 489–500 (2021). https://doi.org/10.1007/s10586-020-03131-0

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  • DOI: https://doi.org/10.1007/s10586-020-03131-0

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