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| 半监督FCM聚类算法目标函数研究 | |
| 引用本文： | 李春芳,庞雅静,钱丽璞,高爱华.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. |
| 作者姓名： | 李春芳 庞雅静 钱丽璞 高爱华 |
| 作者单位： | 1. 北京航空航天大学,自动化科学与电气工程学院,北京,100083;河北体育学院,网络中心,石家庄,050041 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 |
| 基金项目： | 国家高技术研究发展计划(863计划) |
| 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 |
| 收稿时间： | 2008-3-17 |
| 修稿时间： | 2008-7-23 |
Objective function of semi-supervised FCM clustering algorithm |
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| LI Chun-fang,PANG Ya-jing,QIAN Li-pu,GAO Ai-hua.Objective function of semi-supervised FCM clustering algorithm[J].Computer Engineering and Applications,2009,45(14):128-132. | |
| Authors: | LI Chun-fang PANG Ya-jing QIAN Li-pu GAO Ai-hua |
| Affiliation: | LI Chun-fang1,3,PANG Ya-jing2,QIAN Li-pu3,GAO Ai-hua41.School of Automation , Electrical Engineering,Beihang University,Beijing 100083,China 2.School of Architecture Engineering,Hebei University of Science , 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 , Technology,Qinhuangdao,Hebei 066004,China |
| 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. |
| Keywords: | Fuzzy C-Means（FCM） algorithm semi-supervised clustering objective function fuzzy covariance |
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