Abstract The clustering result of k-Prototypes algorithm is unpredictable due to the sensitivity of the initial prototypes selection. Moreover, the whole diversity between data points and clusters is ignored. Therefore, a clustering algorithm based on dimensional frequency dissimilarity and strongly connected fusion is proposed. Plenty of sub-clusters are produced by multiple pre-clustering. According to the connectivity of those sub-clusters, strongly connected fusion is used to generate the final clusters. The proposed clustering algorithm is validated on three different UCI datasets. Meanwhile, it is compared with three mixed data clustering algorithms. The experimental results show that the proposed algorithm can yield better clustering precision and purity.
Fund:Supported by Special Fund for Scientific Research in the Public Welfare of Ministry of Water Resources of China (No.201401044)
About author:: QIAN Chaokai， born in 1990， Master student. His research interests include data mining.HUANG Decai (Corresponding author)， born in 1958， Ph.D.， Professor. His research interests include data mining and artificial intelligence.
QIAN Chaokai,HUANG Decai. Clustering Algorithm for Mixed Data Based on Dimensional Frequency Dissimilarity and Strongly Connected Fusion[J]. , 2016, 29(1): 82-89.
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