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A Big Data Analysis by Between-Cluster Information using k-Modes Clustering Algorithm
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  • Journal title : Journal of Digital Convergence
  • Volume 13, Issue 11,  2015, pp.157-164
  • Publisher : The Society of Digital Policy and Management
  • DOI : 10.14400/JDC.2015.13.11.157
 Title & Authors
A Big Data Analysis by Between-Cluster Information using k-Modes Clustering Algorithm
Park, In-Kyoo;
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This paper describes subspace clustering of categorical data for convergence and integration. Because categorical data are not designed for dealing only with numerical data, The conventional evaluation measures are more likely to have the limitations due to the absence of ordering and high dimensional data and scarcity of frequency. Hence, conditional entropy measure is proposed to evaluate close approximation of cohesion among attributes within each cluster. We propose a new objective function that is used to reflect the optimistic clustering so that the within-cluster dispersion is minimized and the between-cluster separation is enhanced. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four algorithms, using three evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the proposed algorithm outperforms the algorithms that were considered int the evaluation, regarding the considered metrics.
Convergence and Integration;Subspace Partition;Categorical Data;Clustering;Entropy;
 Cited by
상한 융합 변수를 갖는 단선형제약 오목함수 최소화 문제의 해법,오세호;

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