K-modes Clustering Algorithm for Categorical Data
نویسندگان
چکیده
منابع مشابه
A fuzzy k-modes algorithm for clustering categorical data
This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effec...
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The fuzzy k-Modes algorithm introduced by Huang and Ng [Huang, Z., & Ng, M. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), 446–452] is very effective for identifying cluster structures from categorical data sets. However, the algorithm may stop at locally optimal solutions. In order to search for appropriate fuzzy membership matrices...
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Many optimization based clustering algorithms suffer from the possibility of stopping at locally optimal partitions of data sets. In this paper, we present a genetic k-Modes algorithm(GKMODE) that finds a globally optimal partition of a given categorical data set into a specified number of clusters. We introduce a k-Modes operator in place of the normal crossover operator. Our analysis shows th...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2015
ISSN: 0975-8887
DOI: 10.5120/ijca2015906708