一種改進的區(qū)間型不確定數(shù)據(jù)模糊聚類方法
doi: 10.11999/JEIT190591
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湖南工業(yè)大學(xué) 計算機學(xué)院 株洲 412007
An Improved Fuzzy Clustering Method for Interval Uncertain Data
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School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
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摘要:
針對區(qū)間型不確定數(shù)據(jù)的特點，該文提出一種改進的模糊C均值聚類算法(IU-IFCM)。首先對區(qū)間型數(shù)據(jù)進行特征變換，由p維特征映射成由2p維特征組成的實數(shù)據(jù)，然后考慮區(qū)間中值與區(qū)間大小關(guān)系，設(shè)計一種樣本距離計算方法，通過模糊C均值實現(xiàn)對區(qū)間型樣本聚類。理論分析與對比實驗表明，該算法的劃分系數(shù)(PC)及正確等級(CR)值比其它方法平均提高10%以上，表明有更好的聚類精度，對當前大數(shù)據(jù)環(huán)境下不確定數(shù)據(jù)的分類提供了一種新的解決方案。
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關(guān)鍵詞:
- 區(qū)間型數(shù)據(jù) /
- 模糊C均值 /
- 影響因子 /
- 特征變換
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數(shù)據(jù)集
樣本 比重(g/cm3) 冰點(°C) io值 sa值 亞麻油 [0.930 0.935] [–27 –8] [170 204] [118 196] 紫蘇油 [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] 橄欖油 [0.914 0.919] [0 6] [79 90] [187 196] 牛油 [0.860 0.870] [30 38] [40 48] [190 199] 豬油 [0.858 0.864] [22 32] [53 77] [190 202] 
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表 2 4種算法對Fish數(shù)據(jù)集的分類結(jié)果
腐屑性 肉食性 雜食性 草食性 先驗分類 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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表 3 人工數(shù)據(jù)集
參數(shù) 類1 類2 類3 ${m_1}$ 28 60 45 ${m_2}$ 22 30 38 $\sigma _1^2$ 100 9 9 $\sigma _2^2$ 9 144 9
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