Rough Set based K-Modes Clustering Algorithm with Hadoop Cloud Platform

ZHANG Lisheng; ZHANG Jiong; LEI Dajiang

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Rough Set based K-Modes Clustering Algorithm with Hadoop Cloud Platform

ZHANG Lisheng *,ZHANG Jiong #,LEI Dajiang

College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

*Correspondence author

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Funding: This paper is supported by the following foundations or programs, including National Natural Science Foundation of China （No.61073058, 61075019, 61201383), Natural Science Foundation of Chongqing of China）

Opened online:28 April 2015

Accepted by: none

Citation: ZHANG Lisheng,ZHANG Jiong,LEI Dajiang.Rough Set based K-Modes Clustering Algorithm with Hadoop Cloud Platform[OL]. [28 April 2015] http://en.paper.edu.cn/en_releasepaper/content/4639491

In this paper, in order to solve the problems that the traditional K-Modes clustering algorithm cannot efficiently handle massive amounts of data and cannot accurately calculating the dissimilarity between data objects attributes. Based on rough sets and cloud computing, proposed K-Modes clustering algorithm based MapReduce programming model and rough sets. Firstly, using rough set model to recalculate dissimilarity between data object attributes for improving the accuracy of the calculation of distances, then combine the advantages of Hadoop platform and MapReduce programming model, will be parallelized to achieve K-Modes algorithm based on rough sets. Through the experiment, when clustering high dimensional massive data, the improved algorithm reduces the computer time and get an effective clustering results. Experiments show that the proposed algorithm has better stability and scalability.

Keywords:K-Modes clustering; rough set; MapReduce programming model; Hadoop cloud platform

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