Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1128-1138.DOI: 10.11772/j.issn.1001-9081.2023050610
Special Issue: 数据科学与技术
• Data science and technology • Previous Articles Next Articles
Yu DING, Hanlin ZHANG, Rong LUO(), Hua MENG
Received:
2023-05-22
Revised:
2023-07-06
Accepted:
2023-07-14
Online:
2023-08-01
Published:
2024-04-10
Contact:
Rong LUO
About author:
DING Yu, born in 1999, M. S. candidate. Her research interests include machine learning, clustering analysis.Supported by:
通讯作者:
罗荣
作者简介:
丁雨(1999—),女,四川成都人,硕士研究生,主要研究方向:机器学习、聚类分析基金资助:
CLC Number:
Yu DING, Hanlin ZHANG, Rong LUO, Hua MENG. Fuzzy clustering algorithm based on belief subcluster cutting[J]. Journal of Computer Applications, 2024, 44(4): 1128-1138.
丁雨, 张瀚霖, 罗荣, 孟华. 基于信念子簇切割的模糊聚类算法[J]. 《计算机应用》唯一官方网站, 2024, 44(4): 1128-1138.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023050610
类型 | 数据集 | 样本数 | 特征数 | 类别数 |
---|---|---|---|---|
合成数据集 | Aggregation | 788 | 2 | 7 |
R15 | 600 | 2 | 15 | |
Spiral | 312 | 2 | 3 | |
Flame | 240 | 2 | 2 | |
Jain | 373 | 2 | 2 | |
circle | 299 | 2 | 3 | |
happy | 266 | 2 | 3 | |
Compound | 499 | 2 | 6 | |
fourlines | 600 | 2 | 4 | |
S1 | 5 000 | 2 | 15 | |
真实数据集 | Iris | 150 | 4 | 3 |
Car | 1 728 | 6 | 4 | |
Seeds | 210 | 7 | 3 | |
Pima | 768 | 8 | 2 | |
Wine | 178 | 13 | 3 | |
heartdisseaseh | 294 | 13 | 5 | |
congressEW | 435 | 16 | 2 | |
vote | 435 | 16 | 2 | |
labo | 57 | 16 | 2 | |
mfeat-fou | 2 000 | 76 | 10 | |
semeionEW | 1 593 | 256 | 10 | |
americanflag | 873 | 892 | 3 | |
cactus | 919 | 892 | 3 | |
worldmap | 935 | 899 | 3 | |
leukemia | 72 | 7 070 | 2 |
Tab. 1 Details of datasets
类型 | 数据集 | 样本数 | 特征数 | 类别数 |
---|---|---|---|---|
合成数据集 | Aggregation | 788 | 2 | 7 |
R15 | 600 | 2 | 15 | |
Spiral | 312 | 2 | 3 | |
Flame | 240 | 2 | 2 | |
Jain | 373 | 2 | 2 | |
circle | 299 | 2 | 3 | |
happy | 266 | 2 | 3 | |
Compound | 499 | 2 | 6 | |
fourlines | 600 | 2 | 4 | |
S1 | 5 000 | 2 | 15 | |
真实数据集 | Iris | 150 | 4 | 3 |
Car | 1 728 | 6 | 4 | |
Seeds | 210 | 7 | 3 | |
Pima | 768 | 8 | 2 | |
Wine | 178 | 13 | 3 | |
heartdisseaseh | 294 | 13 | 5 | |
congressEW | 435 | 16 | 2 | |
vote | 435 | 16 | 2 | |
labo | 57 | 16 | 2 | |
mfeat-fou | 2 000 | 76 | 10 | |
semeionEW | 1 593 | 256 | 10 | |
americanflag | 873 | 892 | 3 | |
cactus | 919 | 892 | 3 | |
worldmap | 935 | 899 | 3 | |
leukemia | 72 | 7 070 | 2 |
数据集 | 评价指标 | BSCC | BPC | BPEC | FHC-LDP | DPC-DBFN | LDP-MST | DPC-KNN | DPC | SC |
---|---|---|---|---|---|---|---|---|---|---|
Aggregation | ARI | 0.995 6 | 0.781 1 | 0.687 4 | 1.000 0 | 0.992 7 | 0.995 6 | 0.995 6 | 0.754 8 | 0.978 3 |
NMI | 0.992 4 | 0.857 8 | 0.810 6 | 1.000 0 | 0.988 3 | 0.992 4 | 0.992 4 | 0.893 5 | 0.975 4 | |
ACC | 0.997 5 | 0.785 5 | 0.821 1 | 1.000 0 | 0.996 2 | 0.997 5 | 0.997 5 | 0.818 5 | 0.989 8 | |
Arg- | 30/4 | 15 | 25 | 6 | 30 | 2 | 21 | 2 | 4 | |
R15 | ARI | 0.992 8 | 0.992 8 | 0.992 8 | 0.992 8 | 0.996 4 | 0.989 1 | 0.992 8 | 0.992 8 | 0.992 8 |
NMI | 0.994 2 | 0.994 2 | 0.994 2 | 0.994 2 | 0.997 1 | 0.991 3 | 0.994 2 | 0.994 2 | 0.994 2 | |
ACC | 0.996 7 | 0.996 7 | 0.996 7 | 0.996 7 | 0.998 3 | 0.995 0 | 0.996 7 | 0.996 7 | 0.996 7 | |
Arg- | 25/4 | 25 | 15 | 7 | 39 | 2 | 2 | 0.1 | 5 | |
Spiral | ARI | 1.000 0 | 0.235 9 | 0.007 5 | 1.000 0 | 0.148 7 | 1.000 0 | 1.000 0 | 1.000 0 | 1.000 0 |
NMI | 1.000 0 | 0.355 7 | 0.015 9 | 1.000 0 | 0.157 4 | 1.000 0 | 1.000 0 | 1.000 0 | 1.000 0 | |
ACC | 1.000 0 | 0.669 9 | 0.381 4 | 1.000 0 | 0.554 5 | 1.000 0 | 1.000 0 | 1.000 0 | 1.000 0 | |
Arg- | 10/3 | 50 | 85 | 3 | 4 | 2 | 6 | 2 | 2 | |
Flame | ARI | 1.000 0 | 1.000 0 | 0.511 7 | 1.000 0 | 0.966 6 | 0.933 9 | 1.000 0 | 0.586 1 | 0.950 1 |
NMI | 1.000 0 | 1.000 0 | 0.470 0 | 1.000 0 | 0.926 9 | 0.875 2 | 1.000 0 | 0.577 3 | 0.899 1 | |
ACC | 1.000 0 | 1.000 0 | 0.858 3 | 1.000 0 | 0.991 7 | 0.983 3 | 1.000 0 | 0.883 3 | 0.987 5 | |
Arg- | 25/3 | 70 | 45 | 7 | 6 | 2 | 4 | 0.1 | 4 | |
Jain | ARI | 1.000 0 | 0.662 3 | 0.537 6 | 1.000 0 | 0.800 8 | 1.000 0 | 0.757 7 | 0.714 6 | 1.000 0 |
NMI | 1.000 0 | 0.582 3 | 0.520 5 | 1.000 0 | 0.718 8 | 1.000 0 | 0.674 7 | 0.652 2 | 1.000 0 | |
ACC | 1.000 0 | 0.916 9 | 0.868 6 | 1.000 0 | 0.951 7 | 1.000 0 | 0.941 0 | 0.924 9 | 1.000 0 | |
Arg- | 10/5 | 10 | 90 | 7 | 43 | 2 | 37 | 0.5 | 4 | |
circle | ARI | 1.000 0 | 0.510 1 | 0.230 5 | 1.000 0 | 0.460 6 | 1.000 0 | 0.187 9 | 0.214 9 | 1.000 0 |
NMI | 1.000 0 | 0.664 9 | 0.367 2 | 1.000 0 | 0.639 6 | 1.000 0 | 0.323 0 | 0.345 5 | 1.000 0 | |
ACC | 1.000 0 | 0.642 1 | 0.575 3 | 1.000 0 | 0.739 1 | 1.000 0 | 0.602 0 | 0.568 6 | 1.000 0 | |
Arg- | 10/6 | 100 | 15 | 7 | 17 | 2 | 36 | 0.2 | 4 | |
happy | ARI | 1.000 0 | 1.000 0 | 0.526 2 | 1.000 0 | 0.522 1 | 1.000 0 | 0.717 9 | 0.601 7 | 1.000 0 |
NMI | 1.000 0 | 1.000 0 | 0.617 1 | 1.000 0 | 0.609 4 | 1.000 0 | 0.787 1 | 0.679 1 | 1.000 0 | |
ACC | 1.000 0 | 1.000 0 | 0.815 8 | 1.000 0 | 0.815 8 | 1.000 0 | 0.898 5 | 0.849 6 | 1.000 0 | |
Arg- | 10/5 | 45 | 15 | 8 | 2 | 2 | 17 | 0.1 | 4 | |
Compound | ARI | 0.844 3 | 0.777 5 | 0.766 6 | 0.848 3 | 0.808 7 | 0.833 8 | 0.808 7 | 0.546 0 | 0.533 7 |
NMI | 0.897 2 | 0.811 0 | 0.798 3 | 0.855 3 | 0.838 2 | 0.852 0 | 0.850 0 | 0.736 5 | 0.724 6 | |
ACC | 0.854 6 | 0.814 5 | 0.774 4 | 0.882 2 | 0.852 1 | 0.807 0 | 0.869 7 | 0.666 7 | 0.646 6 | |
Arg- | 45/3 | 25 | 15 | 5 | 5 | 2 | 3 | 1 | 21 | |
S1 | ARI | 0.990 6 | 0.988 9 | 0.988 0 | 0.988 9 | 0.988 0 | 0.988 5 | 0.989 7 | 0.989 7 | 0.988 4 |
NMI | 0.990 3 | 0.989 0 | 0.988 6 | 0.988 9 | 0.987 8 | 0.988 7 | 0.989 6 | 0.989 6 | 0.988 8 | |
ACC | 0.995 6 | 0.994 8 | 0.994 4 | 0.994 8 | 0.994 4 | 0.994 6 | 0.995 2 | 0.995 2 | 0.994 8 | |
Arg- | 15/37 | 80 | 55 | 43 | 28 | 2 | 13 | 2 | 36 | |
fourlines | ARI | 1.000 0 | 0.704 0 | 1.000 0 | 1.000 0 | 0.831 1 | 1.000 0 | 0.789 0 | 0.546 5 | 1.000 0 |
NMI | 1.000 0 | 0.821 2 | 1.000 0 | 1.000 0 | 0.875 6 | 1.000 0 | 0.860 5 | 0.758 7 | 1.000 0 | |
ACC | 1.000 0 | 0.745 0 | 1.000 0 | 1.000 0 | 0.933 3 | 1.000 0 | 0.855 0 | 0.650 0 | 1.000 0 | |
Arg- | 10/7 | 30 | 55 | 9 | 35 | 2 | 5 | 0.1 | 6 |
Tab. 2 Clustering performance of nine clustering algorithms on ten synthetic datasets
数据集 | 评价指标 | BSCC | BPC | BPEC | FHC-LDP | DPC-DBFN | LDP-MST | DPC-KNN | DPC | SC |
---|---|---|---|---|---|---|---|---|---|---|
Aggregation | ARI | 0.995 6 | 0.781 1 | 0.687 4 | 1.000 0 | 0.992 7 | 0.995 6 | 0.995 6 | 0.754 8 | 0.978 3 |
NMI | 0.992 4 | 0.857 8 | 0.810 6 | 1.000 0 | 0.988 3 | 0.992 4 | 0.992 4 | 0.893 5 | 0.975 4 | |
ACC | 0.997 5 | 0.785 5 | 0.821 1 | 1.000 0 | 0.996 2 | 0.997 5 | 0.997 5 | 0.818 5 | 0.989 8 | |
Arg- | 30/4 | 15 | 25 | 6 | 30 | 2 | 21 | 2 | 4 | |
R15 | ARI | 0.992 8 | 0.992 8 | 0.992 8 | 0.992 8 | 0.996 4 | 0.989 1 | 0.992 8 | 0.992 8 | 0.992 8 |
NMI | 0.994 2 | 0.994 2 | 0.994 2 | 0.994 2 | 0.997 1 | 0.991 3 | 0.994 2 | 0.994 2 | 0.994 2 | |
ACC | 0.996 7 | 0.996 7 | 0.996 7 | 0.996 7 | 0.998 3 | 0.995 0 | 0.996 7 | 0.996 7 | 0.996 7 | |
Arg- | 25/4 | 25 | 15 | 7 | 39 | 2 | 2 | 0.1 | 5 | |
Spiral | ARI | 1.000 0 | 0.235 9 | 0.007 5 | 1.000 0 | 0.148 7 | 1.000 0 | 1.000 0 | 1.000 0 | 1.000 0 |
NMI | 1.000 0 | 0.355 7 | 0.015 9 | 1.000 0 | 0.157 4 | 1.000 0 | 1.000 0 | 1.000 0 | 1.000 0 | |
ACC | 1.000 0 | 0.669 9 | 0.381 4 | 1.000 0 | 0.554 5 | 1.000 0 | 1.000 0 | 1.000 0 | 1.000 0 | |
Arg- | 10/3 | 50 | 85 | 3 | 4 | 2 | 6 | 2 | 2 | |
Flame | ARI | 1.000 0 | 1.000 0 | 0.511 7 | 1.000 0 | 0.966 6 | 0.933 9 | 1.000 0 | 0.586 1 | 0.950 1 |
NMI | 1.000 0 | 1.000 0 | 0.470 0 | 1.000 0 | 0.926 9 | 0.875 2 | 1.000 0 | 0.577 3 | 0.899 1 | |
ACC | 1.000 0 | 1.000 0 | 0.858 3 | 1.000 0 | 0.991 7 | 0.983 3 | 1.000 0 | 0.883 3 | 0.987 5 | |
Arg- | 25/3 | 70 | 45 | 7 | 6 | 2 | 4 | 0.1 | 4 | |
Jain | ARI | 1.000 0 | 0.662 3 | 0.537 6 | 1.000 0 | 0.800 8 | 1.000 0 | 0.757 7 | 0.714 6 | 1.000 0 |
NMI | 1.000 0 | 0.582 3 | 0.520 5 | 1.000 0 | 0.718 8 | 1.000 0 | 0.674 7 | 0.652 2 | 1.000 0 | |
ACC | 1.000 0 | 0.916 9 | 0.868 6 | 1.000 0 | 0.951 7 | 1.000 0 | 0.941 0 | 0.924 9 | 1.000 0 | |
Arg- | 10/5 | 10 | 90 | 7 | 43 | 2 | 37 | 0.5 | 4 | |
circle | ARI | 1.000 0 | 0.510 1 | 0.230 5 | 1.000 0 | 0.460 6 | 1.000 0 | 0.187 9 | 0.214 9 | 1.000 0 |
NMI | 1.000 0 | 0.664 9 | 0.367 2 | 1.000 0 | 0.639 6 | 1.000 0 | 0.323 0 | 0.345 5 | 1.000 0 | |
ACC | 1.000 0 | 0.642 1 | 0.575 3 | 1.000 0 | 0.739 1 | 1.000 0 | 0.602 0 | 0.568 6 | 1.000 0 | |
Arg- | 10/6 | 100 | 15 | 7 | 17 | 2 | 36 | 0.2 | 4 | |
happy | ARI | 1.000 0 | 1.000 0 | 0.526 2 | 1.000 0 | 0.522 1 | 1.000 0 | 0.717 9 | 0.601 7 | 1.000 0 |
NMI | 1.000 0 | 1.000 0 | 0.617 1 | 1.000 0 | 0.609 4 | 1.000 0 | 0.787 1 | 0.679 1 | 1.000 0 | |
ACC | 1.000 0 | 1.000 0 | 0.815 8 | 1.000 0 | 0.815 8 | 1.000 0 | 0.898 5 | 0.849 6 | 1.000 0 | |
Arg- | 10/5 | 45 | 15 | 8 | 2 | 2 | 17 | 0.1 | 4 | |
Compound | ARI | 0.844 3 | 0.777 5 | 0.766 6 | 0.848 3 | 0.808 7 | 0.833 8 | 0.808 7 | 0.546 0 | 0.533 7 |
NMI | 0.897 2 | 0.811 0 | 0.798 3 | 0.855 3 | 0.838 2 | 0.852 0 | 0.850 0 | 0.736 5 | 0.724 6 | |
ACC | 0.854 6 | 0.814 5 | 0.774 4 | 0.882 2 | 0.852 1 | 0.807 0 | 0.869 7 | 0.666 7 | 0.646 6 | |
Arg- | 45/3 | 25 | 15 | 5 | 5 | 2 | 3 | 1 | 21 | |
S1 | ARI | 0.990 6 | 0.988 9 | 0.988 0 | 0.988 9 | 0.988 0 | 0.988 5 | 0.989 7 | 0.989 7 | 0.988 4 |
NMI | 0.990 3 | 0.989 0 | 0.988 6 | 0.988 9 | 0.987 8 | 0.988 7 | 0.989 6 | 0.989 6 | 0.988 8 | |
ACC | 0.995 6 | 0.994 8 | 0.994 4 | 0.994 8 | 0.994 4 | 0.994 6 | 0.995 2 | 0.995 2 | 0.994 8 | |
Arg- | 15/37 | 80 | 55 | 43 | 28 | 2 | 13 | 2 | 36 | |
fourlines | ARI | 1.000 0 | 0.704 0 | 1.000 0 | 1.000 0 | 0.831 1 | 1.000 0 | 0.789 0 | 0.546 5 | 1.000 0 |
NMI | 1.000 0 | 0.821 2 | 1.000 0 | 1.000 0 | 0.875 6 | 1.000 0 | 0.860 5 | 0.758 7 | 1.000 0 | |
ACC | 1.000 0 | 0.745 0 | 1.000 0 | 1.000 0 | 0.933 3 | 1.000 0 | 0.855 0 | 0.650 0 | 1.000 0 | |
Arg- | 10/7 | 30 | 55 | 9 | 35 | 2 | 5 | 0.1 | 6 |
数据集 | 评价指标 | BSCC | BPC | BPEC | FHC-LDP | DPC-DBFN | LDP-MST | DPC-KNN | DPC | SC |
---|---|---|---|---|---|---|---|---|---|---|
Iris | ARI | 0.903 8 | 0.903 8 | 0.922 2 | 0.903 8 | 0.851 0 | 0.745 5 | 0.903 8 | 0.609 6 | 0.851 5 |
NMI | 0.885 1 | 0.885 1 | 0.901 1 | 0.885 1 | 0.836 6 | 0.798 0 | 0.885 1 | 0.705 1 | 0.862 2 | |
ACC | 0.966 7 | 0.966 7 | 0.973 3 | 0.966 7 | 0.946 7 | 0.900 0 | 0.966 7 | 0.813 3 | 0.946 7 | |
Arg- | 10/4 | 10 | 15 | 9 | 21 | 4 | 2 | 5 | 4 | |
Car | ARI | 0.429 2 | 0.181 8 | 0.117 0 | 0.147 5 | 0.153 5 | -0.021 3 | 0.310 9 | 0.216 5 | 0.255 0 |
NMI | 0.295 5 | 0.220 0 | 0.170 8 | 0.123 7 | 0.101 6 | 0.161 5 | 0.249 4 | 0.328 6 | 0.255 0 | |
ACC | 0.694 4 | 0.480 9 | 0.406 8 | 0.636 6 | 0.608 8 | 0.342 0 | 0.693 3 | 0.483 2 | 0.558 4 | |
Arg- | 25/10 | 95 | 35 | 5 | 50 | 5 | 11 | 0.1 | 10 | |
Seeds | ARI | 0.797 0 | 0.789 5 | 0.715 1 | 0.728 9 | 0.766 4 | 0.570 2 | 0.766 4 | 0.744 8 | 0.826 1 |
NMI | 0.758 4 | 0.750 8 | 0.689 2 | 0.694 2 | 0.734 3 | 0.630 7 | 0.734 3 | 0.719 4 | 0.798 1 | |
ACC | 0.928 6 | 0.923 8 | 0.895 2 | 0.900 0 | 0.914 3 | 0.819 0 | 0.914 3 | 0.904 8 | 0.938 1 | |
Arg- | 15/5 | 30 | 50 | 12 | 2 | 5 | 4 | 0.1 | 4 | |
Pima | ARI | 0.149 7 | 0.013 1 | 0.072 0 | 0.014 3 | 0.078 5 | 0.019 6 | 0.014 3 | 0.141 2 | 0.085 3 |
NMI | 0.092 8 | 0.003 5 | 0.044 7 | 0.004 2 | 0.043 6 | 0.013 2 | 0.004 2 | 0.459 2 | 0.049 3 | |
ACC | 0.696 6 | 0.648 4 | 0.636 7 | 0.649 7 | 0.651 0 | 0.658 9 | 0.649 7 | 0.516 9 | 0.649 7 | |
Arg- | 45/49 | 25 | 40 | 7 | 46 | 4 | 2 | 0.1 | 45 | |
Wine | ARI | 0.865 1 | 0.834 9 | 0.848 5 | 0.726 9 | 0.831 8 | 0.362 7 | 0.726 9 | 0.672 4 | 0.914 9 |
NMI | 0.835 2 | 0.821 5 | 0.816 0 | 0.743 5 | 0.795 3 | 0.432 7 | 0.743 5 | 0.710 4 | 0.892 6 | |
ACC | 0.955 1 | 0.943 8 | 0.949 4 | 0.904 5 | 0.943 8 | 0.713 5 | 0.904 5 | 0.882 0 | 0.971 9 | |
Arg- | 15/20 | 55 | 30 | 17 | 3 | 2 | 17 | 2 | 15 | |
heartdisseaseh | ARI | 0.206 7 | 0.042 1 | 0.172 9 | 0.344 3 | 0.159 6 | 0.178 7 | 0.168 4 | 0.123 3 | 0.134 1 |
NMI | 0.170 9 | 0.101 6 | 0.187 3 | 0.181 5 | 0.191 7 | 0.112 7 | 0.160 1 | 0.177 8 | 0.170 8 | |
ACC | 0.485 7 | 0.445 6 | 0.445 6 | 0.598 6 | 0.438 8 | 0.574 8 | 0.418 4 | 0.370 7 | 0.418 4 | |
Arg- | 85/2 | 90 | 85 | 6 | 13 | 7 | 5 | 0.1 | 3 | |
congressEW | ARI | 0.650 1 | 0.628 1 | 0.037 1 | 0.536 7 | 0.536 3 | 0.557 2 | 0.613 5 | 0.564 1 | 0.592 1 |
NMI | 0.565 1 | 0.547 7 | 0.023 2 | 0.464 3 | 0.432 9 | 0.489 2 | 0.526 5 | 0.505 9 | 0.520 4 | |
ACC | 0.903 4 | 0.896 6 | 0.600 0 | 0.866 7 | 0.866 7 | 0.873 6 | 0.892 0 | 0.875 9 | 0.885 1 | |
Arg- | 30/3 | 30 | 10 | 23 | 2 | 15 | 4 | 2 | 9 | |
vote | ARI | 0.650 0 | 0.620 7 | 0 | 0.564 0 | 0.503 5 | 0.523 2 | 0.650 0 | 0.530 0 | 0.557 2 |
NMI | 0.551 6 | 0.532 1 | 0 | 0.475 8 | 0.463 7 | 0.441 7 | 0.544 7 | 0.459 5 | 0.489 2 | |
ACC | 0.903 4 | 0.894 3 | 0.613 8 | 0.875 9 | 0.855 2 | 0.862 1 | 0.903 4 | 0.864 4 | 0.873 6 | |
Arg- | 65/12 | 30 | 10 | 33 | 4 | 14 | 3 | 2 | 25 | |
labo | ARI | 0.359 0 | 0.317 3 | 0.283 3 | 0.275 4 | 0.073 4 | 0.035 8 | 0.275 4 | 0.241 6 | 0.243 7 |
NMI | 0.254 3 | 0.219 3 | 0.224 8 | 0.184 8 | 0.061 8 | 0.033 8 | 0.184 8 | 0.163 5 | 0.172 9 | |
ACC | 0.807 0 | 0.789 5 | 0.771 9 | 0.771 9 | 0.649 1 | 0.614 0 | 0.771 9 | 0.754 4 | 0.754 4 | |
Arg- | 25/6 | 35 | 45 | 12 | 2 | 7 | 24 | 0.5 | 39 | |
mfeat-fou | ARI | 0.463 5 | 0.305 7 | 0.359 0 | 0.470 8 | 0.145 0 | 0.685 5 | 0.331 9 | 0.363 1 | 0.533 0 |
NMI | 0.657 9 | 0.523 1 | 0.482 7 | 0.610 4 | 0.371 5 | 0.765 1 | 0.523 1 | 0.509 1 | 0.666 1 | |
ACC | 0.630 0 | 0.431 5 | 0.550 5 | 0.639 0 | 0.310 5 | 0.789 5 | 0.484 0 | 0.523 5 | 0.675 4 | |
Arg- | 10/28 | 100 | 50 | 15 | 6 | 50 | 4 | 0.5 | 4 | |
semeionEW | ARI | 0.488 2 | 0.407 7 | 0.304 0 | 0.525 9 | 0.299 7 | 0.446 7 | 0.220 2 | 0.266 2 | 0.567 0 |
NMI | 0.637 0 | 0.579 8 | 0.409 5 | 0.646 5 | 0.442 9 | 0.637 1 | 0.406 7 | 0.426 0 | 0.671 0 | |
ACC | 0.661 0 | 0.531 1 | 0.534 8 | 0.699 3 | 0.480 2 | 0.630 3 | 0.317 6 | 0.425 6 | 0.671 7 | |
Arg- | 15/12 | 90 | 70 | 18 | 3 | 15 | 48 | 0.2 | 3 | |
americanflag | ARI | 0.220 3 | 0.014 8 | -0.014 3 | 0.070 1 | -0.013 6 | -0.020 1 | 0.028 3 | 0.077 3 | 0.011 5 |
NMI | 0.140 3 | 0.029 3 | 0.099 0 | 0.111 5 | 0.048 4 | 0.052 2 | 0.039 6 | 0.052 9 | 0.023 7 | |
ACC | 0.627 7 | 0.463 9 | 0.429 6 | 0.572 7 | 0.378 0 | 0.529 2 | 0.423 8 | 0.497 1 | 0.568 7 | |
Arg- | 20/5 | 25 | 40 | 21 | 6 | 3 | 5 | 2 | 3 | |
cactus | ARI | 0.246 1 | 0.010 3 | 0.102 6 | 0.149 1 | 0.193 6 | 0.188 8 | 0.134 7 | 0.060 1 | 0.013 2 |
NMI | 0.123 9 | 0.012 7 | 0.127 5 | 0.068 7 | 0.089 1 | 0.112 3 | 0.079 7 | 0.048 9 | 0.015 9 | |
ACC | 0.706 2 | 0.664 9 | 0.526 7 | 0.638 7 | 0.669 2 | 0.707 3 | 0.697 5 | 0.551 7 | 0.666 7 | |
Arg- | 30/5 | 10 | 25 | 11 | 20 | 6 | 2 | 0.1 | 6 | |
worldmap | ARI | 0.253 7 | 0.211 9 | 0.141 6 | 0.125 6 | 0.080 1 | 0.224 4 | 0.192 4 | 0.072 1 | 0.005 8 |
NMI | 0.142 6 | 0.109 8 | 0.042 3 | 0.129 3 | 0.021 7 | 0.120 2 | 0.083 1 | 0.038 8 | 0.012 3 | |
ACC | 0.738 0 | 0.713 4 | 0.664 2 | 0.699 5 | 0.591 4 | 0.730 5 | 0.718 7 | 0.503 7 | 0.713 4 | |
Arg- | 60/8 | 30 | 15 | 17 | 46 | 5 | 15 | 0.1 | 3 | |
leukemia | ARI | 0.547 6 | 0.395 0 | 0.268 5 | 0.381 7 | 0.159 5 | 0.292 2 | 0.347 2 | 0.105 2 | 0.117 2 |
NMI | 0.459 4 | 0.282 8 | 0.213 7 | 0.325 0 | 0.107 3 | 0.196 3 | 0.270 4 | 0.143 9 | 0.082 6 | |
ACC | 0.875 0 | 0.819 4 | 0.763 9 | 0.819 4 | 0.708 3 | 0.777 8 | 0.805 6 | 0.708 3 | 0.680 6 | |
Arg- | 20/2 | 10 | 50 | 9 | 43 | 26 | 4 | 1 | 7 |
Tab. 3 Clustering performance of nine clustering algorithms on fifteen real-world datasets
数据集 | 评价指标 | BSCC | BPC | BPEC | FHC-LDP | DPC-DBFN | LDP-MST | DPC-KNN | DPC | SC |
---|---|---|---|---|---|---|---|---|---|---|
Iris | ARI | 0.903 8 | 0.903 8 | 0.922 2 | 0.903 8 | 0.851 0 | 0.745 5 | 0.903 8 | 0.609 6 | 0.851 5 |
NMI | 0.885 1 | 0.885 1 | 0.901 1 | 0.885 1 | 0.836 6 | 0.798 0 | 0.885 1 | 0.705 1 | 0.862 2 | |
ACC | 0.966 7 | 0.966 7 | 0.973 3 | 0.966 7 | 0.946 7 | 0.900 0 | 0.966 7 | 0.813 3 | 0.946 7 | |
Arg- | 10/4 | 10 | 15 | 9 | 21 | 4 | 2 | 5 | 4 | |
Car | ARI | 0.429 2 | 0.181 8 | 0.117 0 | 0.147 5 | 0.153 5 | -0.021 3 | 0.310 9 | 0.216 5 | 0.255 0 |
NMI | 0.295 5 | 0.220 0 | 0.170 8 | 0.123 7 | 0.101 6 | 0.161 5 | 0.249 4 | 0.328 6 | 0.255 0 | |
ACC | 0.694 4 | 0.480 9 | 0.406 8 | 0.636 6 | 0.608 8 | 0.342 0 | 0.693 3 | 0.483 2 | 0.558 4 | |
Arg- | 25/10 | 95 | 35 | 5 | 50 | 5 | 11 | 0.1 | 10 | |
Seeds | ARI | 0.797 0 | 0.789 5 | 0.715 1 | 0.728 9 | 0.766 4 | 0.570 2 | 0.766 4 | 0.744 8 | 0.826 1 |
NMI | 0.758 4 | 0.750 8 | 0.689 2 | 0.694 2 | 0.734 3 | 0.630 7 | 0.734 3 | 0.719 4 | 0.798 1 | |
ACC | 0.928 6 | 0.923 8 | 0.895 2 | 0.900 0 | 0.914 3 | 0.819 0 | 0.914 3 | 0.904 8 | 0.938 1 | |
Arg- | 15/5 | 30 | 50 | 12 | 2 | 5 | 4 | 0.1 | 4 | |
Pima | ARI | 0.149 7 | 0.013 1 | 0.072 0 | 0.014 3 | 0.078 5 | 0.019 6 | 0.014 3 | 0.141 2 | 0.085 3 |
NMI | 0.092 8 | 0.003 5 | 0.044 7 | 0.004 2 | 0.043 6 | 0.013 2 | 0.004 2 | 0.459 2 | 0.049 3 | |
ACC | 0.696 6 | 0.648 4 | 0.636 7 | 0.649 7 | 0.651 0 | 0.658 9 | 0.649 7 | 0.516 9 | 0.649 7 | |
Arg- | 45/49 | 25 | 40 | 7 | 46 | 4 | 2 | 0.1 | 45 | |
Wine | ARI | 0.865 1 | 0.834 9 | 0.848 5 | 0.726 9 | 0.831 8 | 0.362 7 | 0.726 9 | 0.672 4 | 0.914 9 |
NMI | 0.835 2 | 0.821 5 | 0.816 0 | 0.743 5 | 0.795 3 | 0.432 7 | 0.743 5 | 0.710 4 | 0.892 6 | |
ACC | 0.955 1 | 0.943 8 | 0.949 4 | 0.904 5 | 0.943 8 | 0.713 5 | 0.904 5 | 0.882 0 | 0.971 9 | |
Arg- | 15/20 | 55 | 30 | 17 | 3 | 2 | 17 | 2 | 15 | |
heartdisseaseh | ARI | 0.206 7 | 0.042 1 | 0.172 9 | 0.344 3 | 0.159 6 | 0.178 7 | 0.168 4 | 0.123 3 | 0.134 1 |
NMI | 0.170 9 | 0.101 6 | 0.187 3 | 0.181 5 | 0.191 7 | 0.112 7 | 0.160 1 | 0.177 8 | 0.170 8 | |
ACC | 0.485 7 | 0.445 6 | 0.445 6 | 0.598 6 | 0.438 8 | 0.574 8 | 0.418 4 | 0.370 7 | 0.418 4 | |
Arg- | 85/2 | 90 | 85 | 6 | 13 | 7 | 5 | 0.1 | 3 | |
congressEW | ARI | 0.650 1 | 0.628 1 | 0.037 1 | 0.536 7 | 0.536 3 | 0.557 2 | 0.613 5 | 0.564 1 | 0.592 1 |
NMI | 0.565 1 | 0.547 7 | 0.023 2 | 0.464 3 | 0.432 9 | 0.489 2 | 0.526 5 | 0.505 9 | 0.520 4 | |
ACC | 0.903 4 | 0.896 6 | 0.600 0 | 0.866 7 | 0.866 7 | 0.873 6 | 0.892 0 | 0.875 9 | 0.885 1 | |
Arg- | 30/3 | 30 | 10 | 23 | 2 | 15 | 4 | 2 | 9 | |
vote | ARI | 0.650 0 | 0.620 7 | 0 | 0.564 0 | 0.503 5 | 0.523 2 | 0.650 0 | 0.530 0 | 0.557 2 |
NMI | 0.551 6 | 0.532 1 | 0 | 0.475 8 | 0.463 7 | 0.441 7 | 0.544 7 | 0.459 5 | 0.489 2 | |
ACC | 0.903 4 | 0.894 3 | 0.613 8 | 0.875 9 | 0.855 2 | 0.862 1 | 0.903 4 | 0.864 4 | 0.873 6 | |
Arg- | 65/12 | 30 | 10 | 33 | 4 | 14 | 3 | 2 | 25 | |
labo | ARI | 0.359 0 | 0.317 3 | 0.283 3 | 0.275 4 | 0.073 4 | 0.035 8 | 0.275 4 | 0.241 6 | 0.243 7 |
NMI | 0.254 3 | 0.219 3 | 0.224 8 | 0.184 8 | 0.061 8 | 0.033 8 | 0.184 8 | 0.163 5 | 0.172 9 | |
ACC | 0.807 0 | 0.789 5 | 0.771 9 | 0.771 9 | 0.649 1 | 0.614 0 | 0.771 9 | 0.754 4 | 0.754 4 | |
Arg- | 25/6 | 35 | 45 | 12 | 2 | 7 | 24 | 0.5 | 39 | |
mfeat-fou | ARI | 0.463 5 | 0.305 7 | 0.359 0 | 0.470 8 | 0.145 0 | 0.685 5 | 0.331 9 | 0.363 1 | 0.533 0 |
NMI | 0.657 9 | 0.523 1 | 0.482 7 | 0.610 4 | 0.371 5 | 0.765 1 | 0.523 1 | 0.509 1 | 0.666 1 | |
ACC | 0.630 0 | 0.431 5 | 0.550 5 | 0.639 0 | 0.310 5 | 0.789 5 | 0.484 0 | 0.523 5 | 0.675 4 | |
Arg- | 10/28 | 100 | 50 | 15 | 6 | 50 | 4 | 0.5 | 4 | |
semeionEW | ARI | 0.488 2 | 0.407 7 | 0.304 0 | 0.525 9 | 0.299 7 | 0.446 7 | 0.220 2 | 0.266 2 | 0.567 0 |
NMI | 0.637 0 | 0.579 8 | 0.409 5 | 0.646 5 | 0.442 9 | 0.637 1 | 0.406 7 | 0.426 0 | 0.671 0 | |
ACC | 0.661 0 | 0.531 1 | 0.534 8 | 0.699 3 | 0.480 2 | 0.630 3 | 0.317 6 | 0.425 6 | 0.671 7 | |
Arg- | 15/12 | 90 | 70 | 18 | 3 | 15 | 48 | 0.2 | 3 | |
americanflag | ARI | 0.220 3 | 0.014 8 | -0.014 3 | 0.070 1 | -0.013 6 | -0.020 1 | 0.028 3 | 0.077 3 | 0.011 5 |
NMI | 0.140 3 | 0.029 3 | 0.099 0 | 0.111 5 | 0.048 4 | 0.052 2 | 0.039 6 | 0.052 9 | 0.023 7 | |
ACC | 0.627 7 | 0.463 9 | 0.429 6 | 0.572 7 | 0.378 0 | 0.529 2 | 0.423 8 | 0.497 1 | 0.568 7 | |
Arg- | 20/5 | 25 | 40 | 21 | 6 | 3 | 5 | 2 | 3 | |
cactus | ARI | 0.246 1 | 0.010 3 | 0.102 6 | 0.149 1 | 0.193 6 | 0.188 8 | 0.134 7 | 0.060 1 | 0.013 2 |
NMI | 0.123 9 | 0.012 7 | 0.127 5 | 0.068 7 | 0.089 1 | 0.112 3 | 0.079 7 | 0.048 9 | 0.015 9 | |
ACC | 0.706 2 | 0.664 9 | 0.526 7 | 0.638 7 | 0.669 2 | 0.707 3 | 0.697 5 | 0.551 7 | 0.666 7 | |
Arg- | 30/5 | 10 | 25 | 11 | 20 | 6 | 2 | 0.1 | 6 | |
worldmap | ARI | 0.253 7 | 0.211 9 | 0.141 6 | 0.125 6 | 0.080 1 | 0.224 4 | 0.192 4 | 0.072 1 | 0.005 8 |
NMI | 0.142 6 | 0.109 8 | 0.042 3 | 0.129 3 | 0.021 7 | 0.120 2 | 0.083 1 | 0.038 8 | 0.012 3 | |
ACC | 0.738 0 | 0.713 4 | 0.664 2 | 0.699 5 | 0.591 4 | 0.730 5 | 0.718 7 | 0.503 7 | 0.713 4 | |
Arg- | 60/8 | 30 | 15 | 17 | 46 | 5 | 15 | 0.1 | 3 | |
leukemia | ARI | 0.547 6 | 0.395 0 | 0.268 5 | 0.381 7 | 0.159 5 | 0.292 2 | 0.347 2 | 0.105 2 | 0.117 2 |
NMI | 0.459 4 | 0.282 8 | 0.213 7 | 0.325 0 | 0.107 3 | 0.196 3 | 0.270 4 | 0.143 9 | 0.082 6 | |
ACC | 0.875 0 | 0.819 4 | 0.763 9 | 0.819 4 | 0.708 3 | 0.777 8 | 0.805 6 | 0.708 3 | 0.680 6 | |
Arg- | 20/2 | 10 | 50 | 9 | 43 | 26 | 4 | 1 | 7 |
1 | HAN J, KAMBER M, PEI J. 数据挖掘:概念与技术[M]. 范明,孟小峰,译.北京:机械工业出版社,2012:196-222. 10.1016/b978-0-12-381479-1.00005-8 |
HAN J, KAMBER M, PEI J. Data Mining: Concept and Technology [M]. FAN M, MENG X F, translated. Beijing: China Machine Press, 2012: 196-222. 10.1016/b978-0-12-381479-1.00005-8 | |
2 | XIA Y, NIE L, ZHANG L, et al. Weakly supervised multilabel clustering and its applications in computer vision [J]. IEEE Transactions on Cybernetics, 2016, 46(12): 3220-3232. 10.1109/tcyb.2015.2501385 |
3 | SONG Q, WU C, TIAN X, et al. A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation [J]. Applied Intelligence, 2022, 52: 6376-6397. 10.1007/s10489-021-02722-7 |
4 | ARNARSSON I Ö, FROST O, GUSTAVSSON E, et al. Natural language processing methods for knowledge management — applying document clustering for fast search and grouping of engineering documents [J]. Concurrent Engineering, 2021, 29(2): 142-152. 10.1177/1063293x20982973 |
5 | CHAKRABARTI S. Data mining for hypertext: a tutorial survey [J]. ACM SIGKDD Explorations Newsletter, 2000, 1(2): 1-11. 10.1145/846183.846187 |
6 | JOLLIFFE I T, CADIMA J. Principal component analysis: a review and recent developments [J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2016, 374(2065): 20150202. 10.1098/rsta.2015.0202 |
7 | H-S PARK, C-H JUN. A simple and fast algorithm for K-medoids clustering [J]. Expert Systems with Applications, 2009, 36(2): 3336-3341. 10.1016/j.eswa.2008.01.039 |
8 | ESTER M, H-P KRIEGEL, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise [C]// Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Palo Alto: AAAI Press, 1996: 226-231. |
9 | ANKERST M, BREUNIG M M, H-P KRIEGEL, et al. OPTICS: ordering points to identify the clustering structure [J]. ACM SIGMOD Record, 1999, 28(2): 49-60. 10.1145/304181.304187 |
10 | ZHANG T, RAMAKRISHNAN R, LIVNY M. BIRCH: an efficient data clustering method for very large databases [J]. ACM SIGMOD Record, 1996, 25(2): 103-114. 10.1145/235968.233324 |
11 | GUHA S, RASTOGI R, SHIM K. CURE: an efficient clustering algorithm for large databases [J]. ACM SIGMOD Record, 1998, 27(2): 73-84. 10.1145/276305.276312 |
12 | WANG W, YANG J, MUNTZ R R. STING: a statistical information grid approach to spatial data mining [C]// Proceedings of 23rd International Conference on Very Large Data Bases. San Francisco, CA: Morgan Kaufmann Publishers Inc., 1997: 186-195. |
13 | AGRAWAL R, GEHRKE J, GUNOPULOS D, et al. Automatic subspace clustering of high dimensional data for data mining applications [C]// Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. New York: ACM, 1998: 94-105. 10.1145/276305.276314 |
14 | RASMUSSEN C. The infinite Gaussian mixture model [J]. Advances in Neural Information Processing Systems, 1999, 12: 554-560. |
15 | KOHONEN T. The self-organizing map [J]. Proceedings of the IEEE, 1990, 78(9): 1464-1480. 10.1109/5.58325 |
16 | LUXBURG U. A tutorial on spectral clustering [J]. Statistics and Computing, 2007, 17(4): 395-416. 10.1007/s11222-007-9033-z |
17 | SI X, YIN Q, ZHAO X, et al. Robust deep multi-view subspace clustering networks with a correntropy-induced metric [J]. Applied Intelligence, 2022, 52(13): 14871-14887. 10.1007/s10489-022-03209-9 |
18 | NIE F, WANG X, JORDAN M, et al. The constrained laplacian rank algorithm for graph-based clustering [J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2016, 30(1): 1969-1976. 10.1609/aaai.v30i1.10302 |
19 | BAO H Q, VAN HOAI T. A deep embedded clustering algorithm for the binning of metagenomic sequences [J]. IEEE Access, 2022, 10: 54348-54357. 10.1109/access.2022.3176954 |
20 | RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks [J]. Science, 2014, 344(6191): 1492-1496. 10.1126/science.1242072 |
21 | TONG W, LIU S, GAO X-Z. A density-peak-based clustering algorithm of automatically determining the number of clusters [J]. Neurocomputing, 2021, 458: 655-666. 10.1016/j.neucom.2020.03.125 |
22 | DU H, HAO Y, WANG Z. An improved density peaks clustering algorithm by automatic determination of cluster centres [J]. Connection Science, 2022, 34(1): 857-873. 10.1080/09540091.2021.2012422 |
23 | DU M, DING S, JIA H. Study on density peaks clustering based on k-nearest neighbors and principal component analysis [J]. Knowledge-Based Systems, 2016, 99: 135-145. 10.1016/j.knosys.2016.02.001 |
24 | LIU R, WANG H, YU X. Shared-nearest-neighbor-based clustering by fast search and find of density peaks [J]. Information Sciences, 2018, 450: 200-226. 10.1016/j.ins.2018.03.031 |
25 | GUAN J, LI S, HE X, et al. Fast hierarchical clustering of local density peaks via an association degree transfer method [J]. Neurocomputing, 2021, 455: 401-418. 10.1016/j.neucom.2021.05.071 |
26 | CHENG D, ZHU Q, HUANG J, et al. Clustering with local density peaks-based minimum spanning tree [J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(2): 374-387. 10.1109/tkde.2019.2930056 |
27 | 巩树凤,张岩峰.EDDPC:一种高效的分布式密度中心聚类算法[J].计算机研究与发展,2016,53(6):1400-1409. 10.7544/issn1000-1239.2016.20150616 |
GONG S F, ZHANG Y F. EDDPC: an efficient distributed density peaks clustering algorithm[J]. Journal of Computer Research and Development, 2016, 53(6): 1400-1409. 10.7544/issn1000-1239.2016.20150616 | |
28 | 徐晓,丁世飞,孙统风,等.基于网格筛选的大规模密度峰值聚类算法[J].计算机研究与发展,2018,55(11):2419-2429. 10.7544/issn1000-1239.2018.20170227 |
XU X, DING S F, SUN T F, et al. Large-scale density peaks clustering algorithm based on grid screening [J]. Journal of Computer Research and Development, 2018, 55(11): 2419-2429. 10.7544/issn1000-1239.2018.20170227 | |
29 | BEZDEK J C, EHRLICH R, FULL W. FCM: the fuzzy c-means clustering algorithm [J]. Computers & Geosciences, 1984, 10(2/3): 191-203. 10.1016/0098-3004(84)90020-7 |
30 | LEI T, JIA X, ZHANG Y, et al. Superpixel-based fast fuzzy C-means clustering for color image segmentation [J]. IEEE Transactions on Fuzzy Systems, 2019, 27(9): 1753-1766. 10.1109/tfuzz.2018.2889018 |
31 | TANG Y, REN F, PEDRYCZ W. Fuzzy C-means clustering through SSIM and patch for image segmentation [J]. Applied Soft Computing, 2020, 87: 105928. 10.1016/j.asoc.2019.105928 |
32 | M-H MASSON, DENOEUX T. ECM: an evidential version of the fuzzy c-means algorithm [J]. Pattern Recognition, 2008, 41(4): 1384-1397. 10.1016/j.patcog.2007.08.014 |
33 | DENOEUX T, KANJANATARAKUL O. Evidential clustering: a review [C]// Proceedings of the 15th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making. Cham: Springer, 2016: 24-35. 10.1007/978-3-319-49046-5_3 |
34 | XIE J, GAO H, XIE W, et al. Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors [J]. Information Sciences, 2016, 354: 19-40. 10.1016/j.ins.2016.03.011 |
35 | LOTFI A, MORADI P, BEIGY H. Density peaks clustering based on density backbone and fuzzy neighborhood [J]. Pattern Recognition, 2020, 107: 107449. 10.1016/j.patcog.2020.107449 |
36 | BIAN Z, F-L CHUNG, WANG S. Fuzzy density peaks clustering [J]. IEEE Transactions on Fuzzy Systems, 2021, 29(7): 1725-1738. 10.1109/tfuzz.2020.2985004 |
37 | SU Z-G, DENOEUX T. BPEC: belief-peaks evidential clustering [J]. IEEE Transactions on Fuzzy Systems, 2019, 27(1): 111-123. 10.1109/tfuzz.2018.2869125 |
38 | GONG C, SU Z-G, WANG P-H, et al. An evidential clustering algorithm by finding belief-peaks and disjoint neighborhoods [J]. Pattern Recognition, 2021, 113: 107751. 10.1016/j.patcog.2020.107751 |
39 | GONG C, SU Z-G, WANG P-H, et al. Cumulative belief peaks evidential K-nearest neighbor clustering [J]. Knowledge-Based Systems, 2020, 200: No. 105982. 10.1016/j.knosys.2020.105982 |
40 | MENG J, FU D, TANG Y. Belief-peaks clustering based on fuzzy label propagation [J]. Applied Intelligence, 2020, 50(4): 1259-1271. 10.1007/s10489-019-01576-4 |
41 | SHAFER G. A mathematical Theory of Evidence [M]. Princeton: Princeton University Press, 1976: 39-40. |
42 | LONG Z, GAO Y, MENG H, et al. Clustering based on local density peaks and graph cut [J]. Information Sciences, 2022, 600: 263-286. 10.1016/j.ins.2022.03.091 |
43 | XU W, LIU X, GONG Y. Document clustering based on non-negative matrix factorization [C]// Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2003: 267-273. 10.1145/860435.860485 |
44 | YANG Y, XU D, NIE F, et al. Image clustering using local discriminant models and global integration [J]. IEEE Transactions on Image Processing, 2010, 19(10): 2761-2773. 10.1109/tip.2010.2049235 |
45 | STEINLEY D. Properties of the Hubert-Arable adjusted rand index [J]. Psychological Methods, 2004, 9(3): 386-396. 10.1037/1082-989x.9.3.386 |
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