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.

参考文献 16

[1]	JIANG L, DING H F. Improvement of the fuzzy Cmeansclustering algorithm [J]. Computer & DigitalEngineering, 2010, 38(2): 4-6 (in Chinese).
[2]	WANG X,WANG C F, SHEN J Y. Semi-supervised kmeansclustering by optimizing initial cluster centers[J]. Lecture Notes in Computer Science, 2011, 6988:178-187.
[3]	SUN J G, LIU J, ZHAO L Y. Clustering algorithmsresearch [J]. Journal of Software, 2008, 19(1): 48-61(in Chinese).
[4]	LIU X M, LEI D. An improved fuzzy C-means clusteringalgorithm [J]. Journal of Qingdao Universityof Science and Technology (Natural Science Edition),2011, 32(2): 194-198 (in Chinese).
[5]	CHEN L L, AN C L. Adaptive fuzzy C-means algorithmon density and validity [J]. Microcomputer & ItsApplications, 2012, 31(8): 194-198 (in Chinese).
[6]	LI J F, ZHU F F. Improved FCM algorithm basedon the density of samples [J]. Science Technology andEngineering, 2007, 7(4): 636-638 (in Chinese).
[7]	LU C H, XIAO S Q, GU X F. Improving fuzzy Cmeansclustering algorithm based on a density-induceddistance measure [J]. Journal of Engineering, 2014.https://doi.org/10.1049/joe.2014.0053 (published online).
[8]	DUNN J C. A fuzzy relative of the ISODATA processand its use in detecting compact well-separated clusters[J]. Journal of Cybernetics, 1974, 3(3): 32-57.
[9]	BEZDEK J C. Cluster validity with fuzzy sets [J].Journal of Cybernetics, 1974, 3(3): 58-73.
[10]	LIU L F, SUN S Z, YU H Y, et al. A modified fuzzy Cmeans(FCM) clustering algorithm and its applicationon carbonate fluid identification [J]. Journal of AppliedGeophysics, 2016, 129: 28-35.
[11]	YANG M S, NATALIANI Y. Robust-learning fuzzy Cmeansclustering algorithm with unknown number ofclusters [J]. Pattern Recognition, 2017, 71: 45-59.
[12]	HATHAWAY R J, BEZDEK J C, HU Y K. Generalizedfuzzy C-means clustering strategies using Lp norm distances[J]. IEEE Transactions on Fuzzy Systems, 2000,8(5): 576-582.
[13]	BEZDEK J C. Pattern recognition with fuzzy objectivefunction algorithms [M]. New York, USA: PlenumPress, 1981.
[14]	YU F H, XU H K, WANG L M, et al. An improved automaticFCM clustering algorithm [C]//Proceedings ofInternational Workshop on Database Technology andApplications. Wuhan, China: IEEE, 2010: 1-4.
[15]	QIAN Y T, ZHAO R C. Robust clustering based onglobal data distribution and local connectivity matrix[C]//IEEE International Conference on IntelligentProcessing Systems. Beijing, China: IEEE, 1997: 1629-1633.
[16]	PIAO S Z, CHAOMURILIGE, YU J. Cluster validityindexes for FCM clustering algorithm [J]. PatternRecognition & Artificial Intelligence, 2015, 28(5): 452-461 (in Chinese).

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

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

可视化

摘要/Abstract

引用本文

使用本文

参考文献 16

相关文章 6

编辑推荐

Metrics

本文评价

[1]	WU Xin (武星), PU Juan (蒲娟), XIE Shaorong (谢少荣) . Attacking Strategy of Multiple Unmanned Surface Vehicles with Improved GWO Algorithm Under Control of Unmanned Aerial Vehicles[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(2): 201-207.
[2]	WU Xing (武星), LIU Yuan (刘远), XIE Shaorong (谢少荣), GUO Yike (郭毅可). Collaborative Defense with Multiple USVs and UAVs Based on Swarm Intelligence[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(1): 51-56.
[3]	XIANG Zhikang (相志康), LI Min* (李敏), XIAO Liang (肖亮), LIAN Zhichao (练智超), WEI Zhih. Deformable Registration of Chest Radiographs Using B-spline Based Method with Modified Residual Complexity[J]. Journal of Shanghai Jiao Tong University (Science), 2019, 24(2): 226-232.
[4]	LI Da-zhi1,2* (李大志), LIU Jian-hua3 (刘建华), DONG Xin1 (董鑫),LI Lu-qun2 (李鲁群), CHEN . Application-Oriented Cloud Monitoring Data Distribution Mechanism[J]. 上海交通大学学报（英文版）, 2015, 20(2): 149-155.
[5]	LIU Qin (刘钦), WANG Qian*(王乾). Groupwise Registration of Brain Magnetic Resonance Images: A Review[J]. 上海交通大学学报（英文版）, 2014, 19(6): 755-762.
[6]	HUANG Qiang1,2 (黄强), LOU Xin-yuan3 (楼新远), WANG Wei4* (王薇), NI Shao-quan1 (倪少权). Research of Order Allocation Model Based on Cloud and Hybrid Genetic Algorithm Under Ecommerce Environment[J]. 上海交通大学学报（英文版）, 2013, 18(3): 334-342.