New fuzzy clustering algorithm based on feature weighted Mahalanobis distances

Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (5): 198-200.

• 图形、图像、模式识别 • Previous Articles Next Articles

New fuzzy clustering algorithm based on feature weighted Mahalanobis distances

CAI Jingying1, XIE Fuding2, ZHANG Yong2

1.Department of Computer Science and Technology, Mudanjiang Normal University, Mudanjiang, Heilongjiang 157011, China
2.School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning 116081, China

Received:1900-01-01 Revised:1900-01-01 Online:2012-02-11 Published:2012-02-11

基于马氏距离特征加权的模糊聚类新算法

蔡静颖1，谢福鼎2，张永2

1.牡丹江师范学院计算机科学与技术系，黑龙江牡丹江 157011
2.辽宁师范大学计算机与信息学院，辽宁大连 116081

Abstract

Abstract: Fuzzy clustering analysis is an important research field of the fuzzy pattern recognition, and the Fuzzy C-Means algorithm（FCM） is the most classical algorithm. It regards the sample features have the same contribution to the cluster result; not thinking the different features may have different impacts on the cluster result. When FCM processes some datasets of high correlation, error probability will be increased. Focusing on above two problems, this paper proposes an improved new fuzzy clustering algorithm based on feature weighted Mahalanobis distance function. Using adaptive Mahalanobis distance to weight the feature, the new algorithm can effectively cluster to the datasets of high correlation. Experiment illustrates its effectiveness and feasibility.

Key words: Fuzzy C-Means, Mahalanobis distances, correlation of attributes, feature weighted

摘要： 模糊聚类分析是模糊模式识别中一个重要研究领域，而其中最经典的模糊C均值算法认为样本矢量各特征对聚类结果贡献均匀，没有考虑不同的属性特征对模式分类的不同影响，在处理属性高相关的数据集时，该算法分错率增加。针对这些问题，提出了一种基于马氏距离特征加权的模糊聚类算法，利用自适应马氏距离的优点对特征加权处理，对高属性相关的数据集进行更有效的分类。实验证明该方法的可行性和有效性。

关键词: 模糊[C]均值, 马氏距离, 属性相关, 特征加权

CAI Jingying1, XIE Fuding2, ZHANG Yong2. New fuzzy clustering algorithm based on feature weighted Mahalanobis distances[J]. Computer Engineering and Applications, 2012, 48(5): 198-200.

蔡静颖1，谢福鼎2，张永2. 基于马氏距离特征加权的模糊聚类新算法[J]. 计算机工程与应用, 2012, 48(5): 198-200.

[1]	LAN Hong, HUANG Min. Fusion of KNN Optimized Density Peaks and FCM Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(9): 81-88.
[2]	CHEN Tiantian, HE Xiuqing, GE Wenshuang, HE Juhou. Method and Application of Massive Online Collaborative Learning Grouping [J]. Computer Engineering and Applications, 2021, 57(1): 92-98.
[3]	HUANG Jianxin, YUAN Jie. Autonomous Location Strategy of Plume Source in Three-Dimensional Space Using Robot with Active Olfactory [J]. Computer Engineering and Applications, 2020, 56(12): 223-230.
[4]	LIU Juntao, WANG Sujuan, LIN Xiaoming, LIU Zuoshi, TAN Jianhui, SONG Dan. Exudates Detection in Fundus Image Based on Fuzzy C-Means Clustering and Edge-Aware Model [J]. Computer Engineering and Applications, 2020, 56(11): 192-199.
[5]	CUI Fangyi1, JING Xiaoyuan2, DONG Xiwei2，3, WU Fei2, SUN Ying2. Fuzzy Clustering Based on Adaptive Bat Algorithm Optimization and Its Application [J]. Computer Engineering and Applications, 2019, 55(7): 16-22.
[6]	LIU Qiang1, SHI Hong1, WANG Pingxin2，3, YANG Xibei1. Three-Way Clustering Analysis Based on [ε] Neighborhood [J]. Computer Engineering and Applications, 2019, 55(6): 140-144.
[7]	SONG Kai, FENG Xiang, YU Huiqun. New fuzzy clustering based on group evolution strategies [J]. Computer Engineering and Applications, 2018, 54(4): 36-43.
[8]	JIA Wei1，2, HUA Qingyi1, ZHANG Minjun1, CHEN Rui1, JI Xiang1, WANG Bo1，3. Semi-supervised classification of mobile interface pattern using improved extreme learning machine [J]. Computer Engineering and Applications, 2018, 54(2): 11-19.
[9]	WANG Wenhui1, YANG Geng1, GE Wei1, LIU Peidong2, QIAN Chen3. Fuzzy C-Means clustering algorithm under noise uneven condition and its application [J]. Computer Engineering and Applications, 2018, 54(19): 172-178.
[10]	MIAO Lu, LI Hongyan, FENG Xinxi. Multiple extended target tracking algorithm based on CODHD clustering division [J]. Computer Engineering and Applications, 2018, 54(19): 261-265.
[11]	LIU Cangsheng, XU Qinglin. Fuzzy C-means clustering algorithm based on density peak value optimization [J]. Computer Engineering and Applications, 2018, 54(14): 153-157.
[12]	FENG Yufang, LU Houqing, YIN Hong. Fuzzy C-means clustering image segmentation method based on multi-chain quantum bee colony algorithm [J]. Computer Engineering and Applications, 2017, 53(24): 8-14.
[13]	LUO Ling1, YANG You1, MA Yan2. Research on online collaborative learning group division based on Fuzzy C-Means algorithm [J]. Computer Engineering and Applications, 2017, 53(16): 68-73.
[14]	HOU Xiaofan, WU Chengmao. Fast KFCM clustering segmentation algorithm with local information [J]. Computer Engineering and Applications, 2017, 53(13): 189-195.
[15]	HE Jing, WU Chengmao. Kernel space hidden Markov random filed image fuzzy clustering [J]. Computer Engineering and Applications, 2016, 52(22): 185-191.

New fuzzy clustering algorithm based on feature weighted Mahalanobis distances

基于马氏距离特征加权的模糊聚类新算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics