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- W2602445476 abstract "The k -modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k -modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k -modes and Cao’s algorithms." @default.
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- W2602445476 date "2017-01-01" @default.
- W2602445476 modified "2023-10-17" @default.
- W2602445476 title "A Global-Relationship Dissimilarity Measure for the<i>k</i>-Modes Clustering Algorithm" @default.
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- W2602445476 doi "https://doi.org/10.1155/2017/3691316" @default.
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