Improvement of penalty factor in suppressed fuzzy C-means clustering

doi:10.11772/j.issn.1001-9081.2016.09.2427

Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (9): 2427-2431.DOI: 10.11772/j.issn.1001-9081.2016.09.2427

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Improvement of penalty factor in suppressed fuzzy C-means clustering

XIAO Mansheng^1,2, XIAO Zhe¹

1. College of Science and Technology, Hunan University of Technology, Zhuzhou Hunan 412008, China;
2. College of Computer and Communication, Hunan University of Technology, Zhuzhou Hunan 412008, China

Received:2016-03-10 Revised:2016-04-14 Online:2016-09-10 Published:2016-09-08
Supported by:
This work is partially supported by the Natural Science Foundation of Hunan Province (2015JJ2047), the Scientific Research Project of Hunan Provincial Department of Education (13C032, 15A055) and the Scientific Research Project of Hunan University of Technology(2014HXZ28).

抑制式模糊C均值聚类惩罚因子的改进

肖满生^1,2, 肖哲¹

1. 湖南工业大学, 科技学院, 湖南株洲 412008;
2. 湖南工业大学, 计算机与通信学院, 湖南株洲 412008

通讯作者: 肖哲
作者简介:肖满生(1968-),男,湖南邵东人,教授,硕士,主要研究方向:智能计算和智能信息处理;肖哲(1977-),女,湖南澧县人,讲师,硕士,主要研究方向:智能信息处理。
基金资助:
湖南省自然科学基金资助项目（2015JJ2047）；湖南省教育厅项目（13C032）；湖南工业大学科研项目（2014HXZ28）。

Abstract

Abstract: Aiming at the problem of slow convergence and weak real-time processing of large data in general Fuzzy C-Means (FCM) algorithm, an improved method of penalty factor on sample membership was proposed. Firstly, the characteristics of Suppressed Fuzzy C-Means (SFCM) clustering were analyzed, and the trigger condition for adjusting sample membership by penalty factor was studied, and then the dynamic membership adjusting scheme of SFCM based on penalty factor was designed. By using the algorithm, the samples are "moved to the poles" to achieve the purpose of rapid convergence. Theoretical analysis and experimental result show that under the same initial condition, the execution time efficiency of the improved algorithm is increased by 40% and 10% respectively compared with the traditional FCM and Optimal-Selection-based SFCM (OS-SFCM), at the same time, the clustering accuracy is also improved.

Key words: Suppressed Fuzzy C-Means (SFCM), penalty factor, fuzzy membership, fast convergence

摘要： 针对传统模糊C均值（FCM）算法在聚类过程中存在收敛速度慢、对大数据处理实时性不强等问题，提出了一种基于惩罚因子的样本隶属度改进算法。首先分析抑制式模糊C均值（SFCM）聚类特点，研究惩罚因子对样本隶属度修正的触发条件，进而设计出基于惩罚因子的SFCM聚类隶属度动态修正算法。通过算法实现样本向“两极移动”，达到快速收敛之目的。理论分析与实验结果表明，在相同的初始化条件下，改进算法的执行时间效率比传统FCM算法提高约40%，比基于优化选择的SFCM（OS-SFCM）算法提高10%，其聚类准确度与其他两种算法相比也有一定的提高。

关键词: 抑制式模糊C均值, 惩罚因子, 模糊隶属度, 快速收敛

CLC Number:

TP391.4

XIAO Mansheng, XIAO Zhe. Improvement of penalty factor in suppressed fuzzy C-means clustering[J]. Journal of Computer Applications, 2016, 36(9): 2427-2431.

肖满生, 肖哲. 抑制式模糊C均值聚类惩罚因子的改进[J]. 计算机应用, 2016, 36(9): 2427-2431.

References

[1] DUNN J C. Well-separated clusters and the optimal fuzzy partitions [J]. Journal of Cybernet, 1974, 4(1): 95-104.
[2] BEZDEK J C. Pattern Recognition with Fuzzy Objective Function Algorithms [M]. Norwell, MA: Kluwer Academic Publishers, 1981: 34-41.
[3] BEZDEK J C, KELLER J, KRISNAPURAM R, et al. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing [M]. New York: Springer, 1999: 65-69.
[4] FAN J L, ZHEN W Z, XIE W X. Suppressed fuzzy c-means clustering algorithm [J]. Pattern Recognition Letters, 2003, 24(9/10): 1607-1612.
[5] 张锋,赵杰煜,朱绍军. 可区分惩罚控制竞争学习算法[J].模式识别与人工智能,2014,27(5):426-434.(ZHANG F, ZHAO J Y, ZHU S J. Discriminative rival penalization controlled competitive learning algorithm [J]. Pattern Recognition and Artificial Intelligence, 2014, 27(5): 426-434.)
[6] SZILÁGYI L, SZILÁGYI S M. Generalization rules for the suppressed fuzzy c-means clustering algorithm [J]. Neurocomputing, 2014, 139(5223): 298-309.
[7] SZILÁGYI L, SZILÁGYI S M, BENYÓZ. Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm: a study on the competition in c-means clustering models [J]. Soft Computing, 2010, 14(5): 495-505.
[8] HUNG W L, YANG M S, CHEN D H. Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation [J]. Pattern Recognition Letters, 2006, 27(5): 424-438.
[9] HUNG W L, CHEN D H, YANG M S. Suppressed fuzzy-soft learning vector quantization for MRI segmentation [J]. Artificial Intelligence in Medicine, 2011, 52(1): 33-43.
[10] LAN H, JIN S B . An improved suppressed FCM algorithm for image segmentation [J]. Advanced Materials Research, 2013,712/713/714/715: 2349-2353.
[11] ZHAO F, FAN J, LIU H. Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation [J] . Expert System with Applications, 2014, 41(9): 4083-4093.
[12] SZILÁGYI L, SZILÁGYI S M, KISS C. A generalized approach to the suppressed fuzzy c-means algorithm [M]// TORRA V, NARUKAWA Y, DAUMAS M. Modeling Decisions for Artificial Intelligence, LNCS 6408, Berlin: Springer, 2010: 140-151.
[13] NYMA A, KANG M, KWON Y K, et al. A hybrid technique for medical image segmentation [J]. Journal of Biomedicine and Biotechnology, 2012, 2012(4):213-219.
[14] LI Y, LI G. Fast fuzzy c-means clustering algorithm with spatial constraints for image segmentation [C]// Advances in Neural Network Research and Applications, Lecture Notes in Electrical Engineering 67. Berlin: Springer, 2010: 431-438.
[15] SAAD M F, ALIMI A M. Improved modified suppressed fuzzy c-means [C]// Proceedings of the 2010 2nd International Conference on Image Processing Theory Tools and Applications. Piscataway, NJ: IEEE, 2010: 313-318.
[16] 黄建军,谢维信.半抑制式模糊C-均值聚类算法[J].中国体视学与图像分析,2004,9(2):109-113.(HANG J J, XIE W X. Half-suppressed fuzzy c-means clustering algorithm [J]. Chinese Journal of Stereology and Image Analysis, 2004, 9(2): 109-113.)
[17] TSAI H S, HUNG W L, YANG M S. A robust kernel-based fuzzy c-means algorithm by incorporating suppressed and magnified membership for MRI image segmentation [C]// Proceedings of the 4th International Conference of Artificial Intelligence and Computational Intelligence, LNCS 7530. Berlin: Springer, 2012: 744-754.
[18] 兰红,闵乐泉.结合邻域信息的改进抑制式FCM图像分割方法[J].电视技术,2013,37(17):17-21.(LAN H, MIN L Q. Improved suppressed FCM algorithm for image segmentation based on neighborhood information [J]. Video Engineering, 2013, 37(17): 17-21.)
[19] 范九伦.抑制式模糊C-均值聚类研究综述[J].西安邮电大学学报,2014,19(3):1-5.(FAN J L. A brief overview on suppressed fuzzy C-means clustering [J]. Journal of Xi'an University of Posts and Telecommunications, 2014, 19(3): 1-5.)
[20] BACHE K, LICHMAN M. UCI machine learning repository [EB/OL]. [2015-11-19]. http://archive.ics.uci.edu/datasets.html.

Improvement of penalty factor in suppressed fuzzy C-means clustering

抑制式模糊C均值聚类惩罚因子的改进

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