Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

doi:10.1007/s12204-018-1993-y

Journal of Shanghai Jiao Tong University (Science) ›› 2018, Vol. 23 ›› Issue (5): 636-642.doi: 10.1007/s12204-018-1993-y

Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

WU Shaochun (吴绍春), PANG Yijie (庞毅杰), SHAO Sen (邵森), JIANG Keyuan (江科元)

(1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; 2. Department of Computer Information Technology & Graphics, Purdue University Northwest, Hammond 46323, USA)

出版日期:2018-10-01 发布日期:2018-10-07
通讯作者: PANG Yijie (庞毅杰) E-mail:pangyijie pyj@163.com

Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

WU Shaochun (吴绍春), PANG Yijie (庞毅杰), SHAO Sen (邵森), JIANG Keyuan (江科元)

(1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; 2. Department of Computer Information Technology & Graphics, Purdue University Northwest, Hammond 46323, USA)

Online:2018-10-01 Published:2018-10-07
Contact: PANG Yijie (庞毅杰) E-mail:pangyijie pyj@163.com

摘要/Abstract

摘要： This paper presents an advanced fuzzy C-means (FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars’ box-office data, and the classification accuracy of the first class stars achieves 92.625%.

关键词: objective function, clustering center, fuzzy C-means (FCM) clustering algorithm, degree of membership

Abstract: This paper presents an advanced fuzzy C-means (FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars’ box-office data, and the classification accuracy of the first class stars achieves 92.625%.

Key words: objective function, clustering center, fuzzy C-means (FCM) clustering algorithm, degree of membership

中图分类号:

TP 399

WU Shaochun (吴绍春), PANG Yijie (庞毅杰), SHAO Sen (邵森), JIANG Keyuan (江科元). Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance[J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(5): 636-642.

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Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

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