基于证据空间有效性指标的聚类选择性集成

doi:10.11959/j.issn.1000-436x.2015146

Abstract

Abstract:

At first,the concept of evidence space was proposed to overcome the weakness of distance space for describing the complex structure of data sets.And then,the Davies-Bouldin index was extended based on the evidence space proposed.Meanwhile the label-correlation matrix was used to measure the difference of clusters members.At last,the cluster members with better effectiveness and bigger differences were selected for cluster ensemble.The experimental results show that the proposed method is able to improve the effectiveness of cluster ensemble.

Key words: Davies-Bouldin index, evidence space, cluster ensemble selection, co-association matrix

Kai BI,Xiao-dan WANG,Ya-qiong XING. Cluster ensemble selection based on validity index in evidence space[J]. Journal on Communications, 2015, 36(8): 135-145.

Figures/Tables 9

数据集		距离空间			证据空间
数据集	SS	SD	$\frac{S S}{S D}$	SS	SD	$\frac{S S}{S D}$
iris	0.828 8	0.503 9	1.644 8	0.189 5	0.005 1	36.973
wine	0.644 3	0.440 0	1.464 3	0.186 1	0.004 2	43.991
soybean	0.527 2	0.305 8	1.723 7	0.095 4	0.013 6	6.998 9
sonar	0.486 2	0.463 1	1.049 8	0.082 5	0.040 4	2.043 8
ionosphere	0.637 1	0.539 8	1.180 3	0.154 0	0.039 3	3.920 0
vehicle	0.656 2	0.614 0	1.068 8	0.101 9	0.035 3	2.885 7
segment	0.778 3	0.536 9	1.450 0	0.305 8	0.0215	14.234
yeast1	0.798 3	0.743 1	1.074 3	0.120 8	0.048 8	2.478 0
2-ring	0.580 0	0.490 1	1.183 6	0.098 2	0	+∞
2-line	0.818 2	0.358 6	2.281 9	0.112 9	0	+∞
3-gauss	0.829 2	0.493 4	1.680 6	0.163 8	0.001 5	112.773
half-ring	0.767 9	0.282	2.720	0.100 6	0.000	5 030

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数据集		选择规模：10			选择规模：20			选择规模：30
数据集	DB	NMI	DB^ES	DB	NMI	DB^ES	DB	NMI	DB^ES
iris	0.823 8	0.831 3	0.844 7	0.843 6	0.838 7	0.850 3	0.866 8	0.856 6	0.869 9
wine	0.899 4	0.916 3	0.912 8	0.909 9	0.917 9	0.920 2	0.912 9	0.913 7	0.915 4
soybean	0.588 3	0.585 6	0.595 9	0.590 8	0.585 0	0.587 3	0.586 5	0.585 2	0.586 9
sonar	0.540 9	0.616 0	0.639 9	0.563 7	0.601 1	0.567 1	0.580 0	0.571 4	0.565 8
ionosphere	0.633 0	0.628 2	0.627 4	0.615 8	0.624 3	0.630 2	0.607 0	0.625 5	0.620 2
vehicle	0.373 2	0.377 5	0.370 3	0.375 7	0.379 0	0.380 4	0.378 3	0.379 2	0.379 7
segment	0.550 5	0.621 3	0.551 2	0.581 0	0.633 6	0.591 5	0.577 7	0.629 6	0.587 1
yeast1	0.307 5	0.294 1	0.310 8	0.308 2	0.292 3	0.291 1	0.285 2	0.292 5	0.290 1
2-ring	0.693 7	1.000 0	1.000 0	0.919 2	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0
2-line	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0
3-gauss	0.949 6	0.968 9	0.966 1	0.966 2	0.971 3	0.968 0	0.971 7	0.971 3	0.970 9
half-ring	0.879 5	0.950 2	1.000 0	0.856 4	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0

数据集		选择规模：10			选择规模：20			选择规模：30
数据集	DB	NMI	DB^ES	DB	NMI	DB^ES	DB	NMI	DB^ES
iris	0.904 1	0.902 8	0.912 7	0.904 1	0.913 9	0.908 2	0.867 9	0.865 6	0.899 3
wine	0.853 6	0.883 6	0.903 3	0.856 8	0.900 3	0.904 6	0.834 9	0.892 6	0.879 3
soybean	0.710 4	0.590 2	0.599 4	0.825 8	0.595 2	0.587 0	0.825 8	0.619 7	0.579 7
sonar	0.646 5	0.591 7	0.627 3	0.668 4	0.615 5	0.626 8	0.668 4	0.629 7	0.632 2
ionosphere	0.649 9	0.667 9	0.646 3	0.632 5	0.662 5	0.664 4	0.695 0	0.658 5	0.699 8
vehicle	0.382 2	0.385 7	0.384 2	0.379 7	0.356 2	0.382 2	0.374 4	0.347 7	0.344 3
segment	0.613 5	0.621 3	0.620 2	0.600 1	0.612 8	0.610 7	0.610 4	0.614 3	0.620 0
yeast1	0.303 1	0.307 3	0.343 2	0.343 1	0.274 0	0.291 9	0.311 1	0.288 9	0.315 7
2-ring	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0
2-line	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0
3-gauss	0.961 2	0.974 7	0.978 6	0.953 4	0.966 1	0.977 3	0.853 9	0.961 3	0.975 9
half-ring	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0	1.000 0

Cluster ensemble selection based on validity index in evidence space

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数据集	样本数	维数	类别
iris	150	4	3
wine	178	13	3
soybean	306	35	4
sonar	208	60	2
ionosphere	351	33	2
vehicle	846	18	4
segment	2 310	18	7
yeast1	1 484	8	10
2-ring	400	2	2
2-line	400	2	2
3-gauss	300	2	3
half-ring	400	2	2