[1]严静静,张腾飞. 基于自适应的粗糙C-均值聚类算法[J].计算机技术与发展,2016,26(03):67-70.
　YAN Jing-jing,ZHANG Teng-fei. Rough C-means Clustering Algorithm Based on Self-adaption[J].,2016,26(03):67-70.
点击复制

 基于自适应的粗糙C-均值聚类算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年03期
页码:
67-70
栏目:
智能、算法、系统工程
出版日期:
2016-03-10

文章信息/Info

Title:
 Rough C-means Clustering Algorithm Based on Self-adaption
文章编号:
1673-629X（2016)03-0067-04
作者:
 严静静; 张腾飞
 南京邮电大学 自动化学院
Author(s):
 YAN Jing-jing; ZHANG Teng-fei
关键词:
 ; 聚类; 粗糙集; 粗糙C-均值; 簇偏移量
Keywords:
 ; clustering; rough set; rough C-means; offsets of classes
分类号:
TP301.6
文献标志码:
A
摘要:
 粗糙C-均值的提出，首次将粗糙集与聚类算法结合起来。随后，众多学者对其进行了广泛研究。然而，绝大多数算法在研究簇的下近似、边界对象时，使用统一的权重，忽略了这些对象本身的差异性以及对所在簇的贡献。针对此问题，文中提出一种改进的聚类方法。通过样本对象偏移其所在簇心的程度，设定不同的簇偏移量，距离簇心越近的样本对象其簇偏移量越大，反之越小。通过此举以客观描述这些样本对象对其所在簇的贡献，使得最终聚类结果更加精确、簇内更加紧密、簇间更加稀疏。实例计算结果以及通过MATLAB对数据库中IRIS的数据集进行仿真验证，表明提出的改进算法具有一定的可行性。
Abstract:
 Rough C-means is proposed to combine the rough set with clustering algorithm first. In the following,many scholars have been doing extensive research. However,for the objects in the low approximation or boundary,the most of algorithms use unified weights,ig-noring the difference of the objects themselves and the contribution to the classes. Aiming at this problem,an improved clustering method is put forward. Based on degree of objects deviated centroid of clusters,it sets different offsets to highlight these objects on contribution to the classes in this paper,making the result of clustering more precise,intra-classes more close,and inter-classes more sparse. The experi-mental results and simulation verification on IRIS by MATLAB shows the method is feasible.

相似文献/References:

[1]蒋璐璐 王适 王宝成 李慧敏 李鑫慧.一种改进的标记分水岭遥感图像分割方法[J].计算机技术与发展,2010,(01):36.
　JIANG Lu-lu,WANG Shi,WANG Bao-cheng,et al.Segmentation of Remote Sensing Image Based on an Improved Labeling Watershed Algorithm[J].,2010,(03):36.
[2]张甜 罗眉 孟晓红 赵宗涛.一种基于状态特征的航天发射故障诊断技术[J].计算机技术与发展,2010,(01):93.
　ZHANG Tian,LUO Mei,MENG Xiao-hong,et al.A Technology in Fault Diagnosis of Spaceflight Launch Based on State Character[J].,2010,(03):93.
[3]王会颖 章义刚.求解聚类问题的改进人工鱼群算法[J].计算机技术与发展,2010,(03):84.
　WANG Hui-ying,ZHANG Yi-gang.An Improved Artificial Fish- Swarm Algorithm of Solving Clustering Analysis Problem[J].,2010,(03):84.
[4]赵敏 倪志伟 刘斌.K—means与朴素贝叶斯在商务智能中的应用[J].计算机技术与发展,2010,(04):179.
　ZHAO Min,NI Zhi-wei,LIU Bin.Application Research of K - Means Clustering and Naive Bayesian Algorithm in Business Intelligence[J].,2010,(03):179.
[5]吴楠 胡学钢.基于聚类分区的序列模式挖掘算法研究[J].计算机技术与发展,2010,(06):109.
　WU Nan,HU Xue-gang.Research on Clustering Partition-Based Approach of Sequential Pattern Mining[J].,2010,(03):109.
[6]游芳 姜建国 张坤.基于二维属性的高维数据聚类算法研究[J].计算机技术与发展,2009,(05):111.
　YOU Fang,JIANG Jian-guo,ZHANG Kun.Cluster- Algorithm Studies Based on Two- Dimensional Attribute Higher - Dimension Data[J].,2009,(03):111.
[7]刘淑英 程国建 彭方.人工神经生长细胞结构网络在医疗诊断的应用[J].计算机技术与发展,2009,(05):231.
　LIU Shu-ying,CHENG Guo-jian,PENG Fang.Applications of Growing Cell Structures of Artificial Neural Network for Medical Diagnosis[J].,2009,(03):231.
[8]范新 沈闻 丁泉勋 沈洁.基于正例和未标文档的半监督分类研究[J].计算机技术与发展,2009,(06):58.
　FAN Xin,SHEN Wen,DING Quan-xun,et al.Research on Semi- Supervised Classification Based on Positive and Unlabeled Text Document[J].,2009,(03):58.
[9]王园园 倪志伟 赵裕啸 伍章俊.基于决策树的模糊聚类评价算法及其应用[J].计算机技术与发展,2009,(09):232.
　WANG Yuan-yuan,NI Zhi-wei,ZHAO Yu-xiao,et al.Fuzzy Clustering Evaluation Algorithm Based on Decision Tree and Application[J].,2009,(03):232.
[10]段凤玲 李龙澍 曹文婷.具有多态特征和聚类处理的蚁群算法[J].计算机技术与发展,2009,(12):77.
　DUAN Feng-ling,LI Long-shu,CAO Wen-ting.Ant Colony Algorithm with Polymorphism and Clustering Processing[J].,2009,(03):77.
[11]李荟,谢强,丁秋林. 一种基于情景的协同过滤推荐算法[J].计算机技术与发展,2014,24(10):42.
　LI Hui,XIEQiang,DING Qiu-lin. A Collaborative Filtering Recommendation Algorithm Based on Scenario[J].,2014,24(03):42.
[12]吴楠,秦锋,姜太平. 一种基于用户情境聚类的个性化推荐算法[J].计算机技术与发展,2014,24(10):106.
　WU Nan,QIN Feng,JIANG Tai-ping. A Personalized Recommendation Algorithm Based on User Context Clustering[J].,2014,24(03):106.
[13]高蕾[],曹建忠[]. 基于可穿戴传感器的行为识别随机逼近模型[J].计算机技术与发展,2014,24(12):83.
　GAO Lei[],CAO Jian-zhong[]. Activity Recognition Using Stochastic Approximation Model Based on Wearable Sensor[J].,2014,24(03):83.
[14]邵明来,秦亮曦. 集粒度计算、蚁群算法与模糊思想的聚类算法[J].计算机技术与发展,2015,25(02):78.
　SHAO Ming-lai,QIN Liang-xi. Clustering Algorithm Combined Granular Computing,Ant Colony Algorithm and Fuzzy Idea[J].,2015,25(03):78.
[15]刘华春,侯向宁,杨忠. 基于聚类与关联的入侵检测系统研究设计[J].计算机技术与发展,2015,25(07):133.
　LIU Hua-chun,HOU Xiang-ning,YANG Zhong. Research and Design of Intrusion Detection System Based on Association and Clustering[J].,2015,25(03):133.
[16]王玉雷,李玲娟. 一种密度和划分结合的聚类算法[J].计算机技术与发展,2015,25(09):53.
　WANG Yu-le,LI Ling-juan. A Clustering Algorithm of Combination of Density and Division[J].,2015,25(03):53.
[17]王伟,李玲娟. 一种基于聚类的社团划分算法[J].计算机技术与发展,2015,25(10):119.
　WANG Wei,LI Ling-juan. A Clustering-based Community Division Algorithm[J].,2015,25(03):119.
[18]余琨,荆晓远,吴飞,等. 基于竞争聚集的K-SVD字典学习算法[J].计算机技术与发展,2015,25(11):44.
　YU Kun,JING Xiao-yuan,WU Fei,et al. K-SVD Dictionary Learning Algorithm Based on Competitive Agglomeration[J].,2015,25(03):44.
[19]李振,贾瑞玉. 一种改进的K-means蚁群聚类算法[J].计算机技术与发展,2015,25(12):28.
　LI Zhen,JIA Rui-yu. An Improved K-means Ant Colony Clustering Algorithm[J].,2015,25(03):28.
[20]郭涵阳,高曼如,沈良忠. Moodle平台师生访问行为日志统计与挖掘研究[J].计算机技术与发展,2016,26(11):168.
　GUO Han-yang,GAO Man-ru,SHEN Liang-zhong. Research on Statistics and Mining of Log Data about Visiting Behavior for Both Teachers and Students from Moodle[J].,2016,26(03):168.

更新日期/Last Update: 2016-06-12