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http://dx.doi.org/10.7583/JKGS.2018.18.1.105

Extended Information Entropy via Correlation for Autonomous Attribute Reduction of BigData  

Park, In-Kyu (Dept. of Game Software, College of Engineering Joongbu University)
Publication Information
Journal of Korea Game Society / v.18, no.1, 2018 , pp. 105-114 More about this Journal
Abstract
Various data analysis methods used for customer type analysis are very important for game companies to understand their type and characteristics in an attempt to plan customized content for our customers and to provide more convenient services. In this paper, we propose a k-mode cluster analysis algorithm that uses information uncertainty by extending information entropy to reduce information loss. Therefore, the measurement of the similarity of attributes is considered in two aspects. One is to measure the uncertainty between each attribute on the center of each partition and the other is to measure the uncertainty about the probability distribution of the uncertainty of each property. In particular, the uncertainty in attributes is taken into account in the non-probabilistic and probabilistic scales because the entropy of the attribute is transformed into probabilistic information to measure the uncertainty. The accuracy of the algorithm is observable to the result of cluster analysis based on the optimal initial value through extensive performance analysis and various indexes.
Keywords
Information Entropy; K-modes Clustering; Categorical Data; Similarity; Uncertainty;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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