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.
钱潮恺，黄德才. 基于维度频率相异度和强连通融合的混合数据聚类算法*[J]. 模式识别与人工智能, 2016, 29(1): 82-89.
QIAN Chaokai, HUANG Decai. Clustering Algorithm for Mixed Data Based on Dimensional Frequency Dissimilarity and Strongly Connected Fusion. , 2016, 29(1): 82-89.
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2016, Vol. 29 
