An Improved Fuzzy Clustering Method for Interval Uncertain Data
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摘要:
针对区间型不确定数据的特点，该文提出一种改进的模糊C均值聚类算法(IU-IFCM)。首先对区间型数据进行特征变换，由p维特征映射成由2p维特征组成的实数据，然后考虑区间中值与区间大小关系，设计一种样本距离计算方法，通过模糊C均值实现对区间型样本聚类。理论分析与对比实验表明，该算法的划分系数(PC)及正确等级(CR)值比其它方法平均提高10%以上，表明有更好的聚类精度，对当前大数据环境下不确定数据的分类提供了一种新的解决方案。
Abstract: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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Key words:
- Interval data /
- Fuzzy C-means /
- Impact factor /
- Feature transformation
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表 1 Fat_Oil数据集
样本 比重(g/cm3) 冰点(°C) io值 sa值 亚麻油 [0.930 0.935] [–27 –8] [170 204] [118 196] 紫苏油 [0.930 0.937] [–5 –4] [192 208] [188 197] 棉籽油 [0.916 0.918] [–6 –1] [99 113] [189 198] 芝麻油 [0.920 0.926] [–6 –4] [104 116] [187 193] 山茶油 [0.916 0.917] [–21 –15] [80 82] [189 193] 橄榄油 [0.914 0.919] [0 6] [79 90] [187 196] 牛油 [0.860 0.870] [30 38] [40 48] [190 199] 猪油 [0.858 0.864] [22 32] [53 77] [190 202] 
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表 2 4种算法对Fish数据集的分类结果
腐屑性 肉食性 杂食性 草食性 先验分类 1 2 3 4 5 6 7 8 9 10 11 12 E_FCM 1 2 5 4 6 3 7 10 8 9 11 12 M_FCM 1 3 4 6 10 11 2 8 5 7 9 12 D_FCM 1 2 4 5 6 8 9 3 10 11 7 12 IU_IFCM 1 2 3 4 6 7 8 5 9 10 11 12
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