Objective function of semi-supervised FCM clustering algorithm

doi:10.3778/j.issn.1002-8331.2009.14.039

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (14): 128-132.DOI: 10.3778/j.issn.1002-8331.2009.14.039

• 数据库、信号与信息处理 • Previous Articles Next Articles

Objective function of semi-supervised FCM clustering algorithm

LI Chun-fang^1,3,PANG Ya-jing²,QIAN Li-pu³,GAO Ai-hua⁴

1.School of Automation and Electrical Engineering，Beihang University，Beijing 100083，China
2.School of Architecture Engineering，Hebei University of Science and Technology，Shijiazhuang 050018，China
3.Network Center，Hebei Institute of Physical Education，Shijiazhuang 050041，China
4.School of E&A，Hebei Normal University of Science and Technology，Qinhuangdao，Hebei 066004，China

Received:2008-03-17 Revised:2008-07-23 Online:2009-05-11 Published:2009-05-11
Contact: LI Chun-fang

半监督FCM聚类算法目标函数研究

李春芳^1,3,庞雅静²,钱丽璞³,高爱华⁴

1.北京航空航天大学自动化科学与电气工程学院，北京 100083
2.河北科技大学建筑工程学院，石家庄 050018
3.河北体育学院网络中心，石家庄 050041
4.河北科技师范学院欧美学院，河北秦皇岛 066004

通讯作者: 李春芳

Abstract

Abstract: Analyze the physical interpretation of objective function of semi-supervised FCM algorithm and the coefficient α.Illustrate that Stutz’s modification to the objective function provided by Pedrycz is more clear，and when α=1，0，the SS-FCM degrades to FCM.Provide the corresponding alternatively optimizing algorithm of SS-FCM.The experimental results show that：（1）Modified algorithm has same semi-supervised function and has more clear physical interpretation.（2）Using FCM algorithm to assign membership for labeled samples is better than using random number.（3）SS-FCM with fuzzy covariance and a small number of good-selected labeled samples can effectively improve the accuracy and convergence rate.

Key words: Fuzzy C-Means（FCM） algorithm, semi-supervised clustering, objective function, fuzzy covariance

摘要： 分析了现有半监督FCM算法目标函数的物理意义和平衡系数α的选取，说明Stutz对Pedrycz目标函数的修改使半监督的物理意义更清楚，它在α=1，0时均退化为标准FCM算法，给出了修改后SS-FCM算法的交替求解过程。实验结果：（1）修改算法与Pedrycz算法有相同的半监督作用和清楚的物理解释；（2）对labeled样本采用FCM算法赋值比用随机数的收敛稳定性高；（3）优选的少量labeled样本，使用模糊协方差的SS-CFCM算法提高了聚类准确性和收敛速度。

关键词: 模糊C均值（FCM）算法, 半监督聚类, 目标函数, 模糊协方差

LI Chun-fang^1,3,PANG Ya-jing²,QIAN Li-pu³,GAO Ai-hua⁴. Objective function of semi-supervised FCM clustering algorithm[J]. Computer Engineering and Applications, 2009, 45(14): 128-132.

李春芳^1,3,庞雅静²,钱丽璞³,高爱华⁴. 半监督FCM聚类算法目标函数研究[J]. 计算机工程与应用, 2009, 45(14): 128-132.

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Objective function of semi-supervised FCM clustering algorithm

半监督FCM聚类算法目标函数研究

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