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簇間可分的魯棒模糊C均值聚類算法

高云龍,  楊程宇,  王志豪,  羅斯哲,  潘金艷

高云龍, 楊程宇, 王志豪, 羅斯哲, 潘金艷. 簇間可分的魯棒模糊C均值聚類算法[J]. 電子與信息學(xué)報, 2019, 41(5): 1114-1121. doi: 10.11999/JEIT180604
引用本文: 高云龍, 楊程宇, 王志豪, 羅斯哲, 潘金艷. 簇間可分的魯棒模糊C均值聚類算法[J]. 電子與信息學(xué)報, 2019, 41(5): 1114-1121. doi: 10.11999/JEIT180604
Yunlong GAO, Chengyu YANG, Zhihao WANG, Sizhe LUO, Jinyan PAN. Robust Fuzzy C-means Clustering Algorithm Integrating Between-cluster Information[J]. Journal of Electronics & Information Technology, 2019, 41(5): 1114-1121. doi: 10.11999/JEIT180604
Citation: Yunlong GAO, Chengyu YANG, Zhihao WANG, Sizhe LUO, Jinyan PAN. Robust Fuzzy C-means Clustering Algorithm Integrating Between-cluster Information[J]. Journal of Electronics & Information Technology, 2019, 41(5): 1114-1121. doi: 10.11999/JEIT180604

簇間可分的魯棒模糊C均值聚類算法

doi: 10.11999/JEIT180604
  • 1.

    廈門大學(xué)航空航天學(xué)院 ??廈門 ??361102

  • 2.

    集美大學(xué)信息工程學(xué)院 ??廈門 ??361021

基金項目: 國家自然科學(xué)基金(61203176)，福建省自然科學(xué)基金(2013J05098, 2016J01756)
詳細信息
    作者簡介:

    高云龍：男，1979年生，副教授，研究方向為機器學(xué)習(xí)、時間序列分析和生產(chǎn)制造系統(tǒng)優(yōu)化與調(diào)度

    楊程宇：男，1996年生，本科生，研究方向為機器學(xué)習(xí)

    王志豪：男，1993年生，碩士生，研究方向為模式識別和機器學(xué)習(xí)

    羅斯哲：男，1995年生，碩士生，研究方向為維數(shù)約簡、模式識別和機器學(xué)習(xí)

    潘金艷：女，1978年生，副教授，研究方向為人工智能和機器學(xué)習(xí)理論與方法

    通訊作者:

    潘金艷　gaoyl@xmu.edu.cn

  • 中圖分類號: TP311.13

Robust Fuzzy C-means Clustering Algorithm Integrating Between-cluster Information

  • 1.

    School of Aerospace Engineering, Xiamen University, Xiamen 361102, China

  • 2.

    Information Engineering College, Jimei University, Xiamen 361021, China

Funds: The National Natural Science Foundation of China (61203176), The Natural Science Foundation of Fujian Province (2013J05098, 2016J01756)
  • 摘要:

    與經(jīng)典的K均值聚類算法相比，模糊C均值(FCM)聚類算法通過引入模糊因子，考慮不同聚類數(shù)據(jù)簇之間的相互關(guān)系，得到可分性更好的聚類結(jié)果。但是模糊因子的引入，使得任意一個樣本點都存在模糊性，造成FCM極易受到噪聲和離群點的影響，聚類結(jié)果泛化性能較差。因此，該文提出一種簇間可分的魯棒FCM算法(RBI-FCM)。RBI-FCM利用K均值算法對模糊隸屬度的稀疏特征，降低不同數(shù)據(jù)簇之間的相互作用，突出不同數(shù)據(jù)簇相鄰區(qū)域的可分性；另外，RBI-FCM在極小化數(shù)據(jù)簇內(nèi)部散布度的條件下，考慮不同數(shù)據(jù)簇之間的可分性，可提高聚類模型的泛化性能。該文設(shè)計了有效的模型求解迭代算法。實驗結(jié)果表明，RBI-FCM算法提高了FCM的魯棒性，有效降低FCM對數(shù)據(jù)簇分布差異性和抽樣不均衡的敏感性，得到理想的聚類結(jié)果。

    Abstract:

    Comparing with K-means, Fuzzy logic is introduced in Fuzzy C-Means to handle the information between clusters. It can obtain better cluster results. However, fuzzy logic makes observations could belong to more than just one cluster, which results FCM is especially sensitivity to the noisy and outlier and has poor generalization performance. So a Rrobust Fuzzy C-Means clustering integrated Between-cluster Information algorithm (RBI-FCM) is proposed. Taking advantage of the sparsity of K-means, RBI-FCM helps to reduce the interactions among different clusters and improve the separability of sample points which locate in the adjacent domains of different clusters. Beside minimizing the inner-cluster scattering condition, RBI-FCM considers the between-cluster information. The generalization performance of RBI-FCM can be improved. An effective iterative algorithm for solving the model is designed in this paper. The experimental results show that the RBI-FCM improves the robustness of FCM and reduce effectively its sensitivity to size-imbalance and differences on the distribution of clusters of FCM. The great clustering result is obtained.

    • HTML全文

  • 圖  1  聚類結(jié)果最大隸屬度值曲線分布情況

    圖  2  人造樣本疏密分布數(shù)據(jù)集

    圖  3  聚類結(jié)果正確率曲線

    圖  4  人造樣本容量分布不均數(shù)據(jù)集

    圖  5  聚類結(jié)果正確率曲線

    圖  6  人造非球形樣本數(shù)據(jù)集及聚類結(jié)果

    表  1  實驗1：人造樣本數(shù)據(jù)集主要參數(shù)

    樣本集類中心協(xié)方差矩陣各類樣本數(shù)
    1(5, 5), (15, 15)[1 0; 0 1], [1 0; 0 1]50, 50
    2(5, 5), (15, 15)[1 0; 0 1], [2 0; 0 2]50, 50
    $\vdots $$\vdots $$\vdots $$\vdots $
    10(5, 5), (15, 15)[1 0; 0 1], [10 0; 0 10]50, 50
    下載: 導(dǎo)出CSV

    表  2  實驗2：人造樣本數(shù)據(jù)集主要參數(shù)

    樣本集樣本隨機分布的圓心各類樣本數(shù)
    1(5, 5), (15, 15)50, 50
    2(5, 5), (15, 15)50, 51
    $\vdots $$\vdots $　　$\vdots $$\vdots $
    151(5, 5), (15, 15)50, 200
    下載: 導(dǎo)出CSV

    表  3  UCI數(shù)據(jù)集聚類實驗的NMI正確率和RI正確率

    UCI數(shù)據(jù)集FCMPFCMGIFP-FCMRBI-FCMUCI數(shù)據(jù)集FCMPFCMGIFP-FCMRBI-FCM
    Auto-mgp0.51900.51670.50080.5443Wine0.41690.41680.39460.4911
    0.75340.75370.75050.78950.71040.71050.67000.7287
    Zoo0.67600.68240.62840.6873Balance Scale0.12230.12320.12930.1326
    0.83810.84000.82360.84640.58870.59000.58060.5947
    Parkinsons0.09260.09360.05260.1071House Votes0.47430.47430.29170.4948
    0.59340.59340.56930.62660.77520.77520.66880.7890
    Credit Approval0.03040.03040.03650.1020Vowel0.30190.31270.33570.3737
    0.50480.50480.52070.54480.77550.79880.82750.8153
    Banknote Authentication0.02920.02920.11450.5249Mammographic Masses0.10540.10650.10200.1130
    0.52360.52360.55550.80530.56760.56830.55240.5746
    注：每個數(shù)據(jù)集實驗結(jié)果的第1行為NMI正確率，第2行為RI正確率
    下載: 導(dǎo)出CSV
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  • 收稿日期:  2018-06-20
  • 修回日期:  2018-12-24
  • 網(wǎng)絡(luò)出版日期:  2018-12-28
  • 刊出日期:  2019-05-01

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