| Citation: | Mansheng XIAO, Longxin ZHANG, Xiaoli ZHANG, Yongxiang HU. An Improved Fuzzy Clustering Method for Interval Uncertain Data[J]. Journal of Electronics & Information Technology, 2020, 42(8): 1968-1974. doi: 10.11999/JEIT190591
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An Improved Fuzzy C-Means clustering algorithm (IU-IFCM) is proposed in this study in accordance with the characteristics of Interval Uncertain data. First, the interval data is transformed into real data composed of 2p dimension feature, which is mapped from that of p dimension feature. Second, a method for calculating sample distance, which realizes the interval sample clustering by fuzzy c-mean algorithm, is designed while considering the relationship between interval median value and interval size. Theoretical analysis and comparison experiments show that the presented algorithm surpaes the compared algorithms by more than 10% on average in terms of the Partition Coefficient (PC) and Correct Rank(CR) value. These results indicate that the algorithm presents in this study has better clustering accuracy and provides a new solution for the classification of uncertain data in current big data environments.
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