结合FCS的多视图模糊聚类算法

doi:10.19665/j.issn1001-2400.2019.04.014

Abstract

Abstract:

By synthesizing different representations of data, multi-view fuzzy clustering can produce more comprehensive and macroscopic clustering results. However it is vulnerable to noise. In order to improve the ability to resist noise, a multi-view fuzzy clustering algorithm is proposed which, inheriting the advantages of multi-view clustering and fuzzy compactness and separation clustering, can collaborate clustering according to the importance of different views and enhance robustness. In order to validate the effectiveness of this algorithm, four multi-view data sets are selected to carry out experiments. Experimental results show that this algorithm can not only achieve high clustering accuracy, but also effectively reduce the impact of noise data on clustering results.

Key words: multi-view, fuzzy clustering, compactness, separation

CLC Number:

TP391.1

LIU Yongli,GUO Chengyi,LIU Jing,WU Yan. Multi-view fuzzy clustering algorithm using FCS[J].Journal of Xidian University, 2019, 46(4): 99-106.

Figures/Tables 9

References 15

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算法	无噪声			有噪声
算法	Accuracy	F-Measure	NMI	Accuracy	F-Measure	NMI
FCS 1	0.314 2	0.270 8	0.244 4	0.347 6	0.282 0	0.218 6
FCS 2	0.580 9	0.564 5	0.638 4	0.595 2	0.524 3	0.681 8
FCS	0.600 0	0.580 0	0.611 2	0.580 9	0.562 8	0.626 6
RMKMC	0.623 8	0.611 0	0.593 0	0.580 9	0.576 7	0.562 8
MinimaxFCM	0.642 8	0.645 6	0.642 2	0.571 4	0.556 6	0.629 0
MvFCS	0.661 9	0.648 1	0.649 2	0.666 6	0.668 8	0.659 1

算法	无噪声			有噪声
算法	Accuracy	F-Measure	NMI	Accuracy	F-Measure	NMI
FCS 1	0.550 5	0.530 9	0.549 3	0.448 0	0.416 0	0.457 5
FCS 2	0.748 0	0.746 1	0.730 3	0.733 0	0.731 3	0.692 8
FCS	0.761 0	0.748 2	0.762 6	0.788 5	0.781 1	0.775 0
RMKMC	0.804 0	0.792 0	0.774 2	0.770 0	0.758 1	0.791 5
MinimaxFCM	0.912 0	0.912 1	0.836 4	0.801 5	0.787 1	0.786 5
MvFCS	0.913 0	0.913 1	0.838 1	0.900 0	0.900 6	0.828 8

算法	无噪声			有噪声
算法	Accuracy	F-Measure	NMI	Accuracy	F-Measure	NMI
FCS 1	0.476 1	0.455 6	0.444 4	0.428 5	0.409 8	0.440 3
FCS 2	0.683 4	0.684 3	0.553 5	0.619 0	0.603 9	0.585 6
FCS	0.789 9	0.764 1	0.848 2	0.773 1	0.748 5	0.818 8
RMKMC	0.938 3	0.848 2	0.884 2	0.778 7	0.749 8	0.783 6
MinimaxFCM	0.941 1	0.862 1	0.893 6	0.770 3	0.734 3	0.798 9
MvFCS	0.952 3	0.947 0	0.901 8	0.949 5	0.944 4	0.899 2

算法	无噪声			有噪声
算法	Accuracy	F-Measure	NMI	Accuracy	F-Measure	NMI
FCS 1	0.325 0	0.282 4	0.125 2	0.317 2	0.276 7	0.124 6
FCS 2	0.747 7	0.747 1	0.598 0	0.745 1	0.747 4	0.601 7
FCS	0.724 3	0.727 5	0.562 9	0.724 3	0.650 4	0.562 9
RMKMC	0.690 5	0.692 4	0.543 8	0.625 4	0.624 3	0.550 2
MinimaxFCM	0.725 6	0.728 7	0.568 3	0.648 8	0.649 4	0.579 4
MvFCS	0.728 2	0.731 3	0.570 5	0.672 3	0.672 8	0.585 6

Multi-view fuzzy clustering algorithm using FCS

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References 15

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