An Improved Semi-supervised FCM Clustering Method for Mixed Data Sets

作者：Li, Xiao Qing; Tang, Hao^*; Si, Jia Sheng; Miao, Gang Zhong

来源：Acta Automatica Sinica, 2018, 44(12): 2259-2268.

DOI：10.16383/j.aas.2018.c170510

• Clustering accuracy
• Dissimilarity threshold
• Fuzzy C mean
• Fuzzy C-means algorithms
• Improved distance
• Jaccard
• Mixed attributes
• Traditional clustering

This paper puts forward a semi-supervised fuzzy C-means (FCM) algorithm based on an improved distance measure to solve the problem of low accuracy of clustering algorithm of data sets with mixed attributes. First, the classification attributes are preprocessed in the data set, and the corresponding dissimilarity threshold is set. Then the traditional clustering distance measure is combined with the improved Jaccard distance measure to determine the distance measure function. Finally, the distance measure function is combined with the traditional semi-supervised FCM algorithm, and clustering is carried out on the characteristic data sets of different coupling fault data of rolling bearings. Simulation results show that the algorithm can achieve better clustering accuracy in mixed data sets.

• 出版日期2018
• 单位合肥工业大学