摘要
粗糙集理论是一种处理边界对象不确定的有效方法。将粗糙集与K均值结合的粗糙K均值聚类算法,具有简单高效且可处理聚类边界元素的特点,但同时存在缺陷。针对粗糙K均值聚类算法对初始点敏感,经验权重设置忽略数据差异性,阈值设置不合理导致聚类结果波动性大的缺陷,本文提出结合蚁群算法的改进粗糙K均值聚类算法,改进的算法中使用蚁群算法中随机概率选择策略和信息素更新的正负反馈机制,以及采用动态调整算法阈值和相关权重的方法,对粗糙K均值聚类算法进行优化。最后采用UCI的Iris、Balance?scale和Wine数据集分别对算法进行实验。实验结果表明,改进后的粗糙K均值聚类算法得到的聚类结果准确率更高。
Rough set theory is an effective method for dealing with uncertain boundary objects.The rough K-means clustering algorithm which combines rough set with K-means is simple and efficient.Though it can deal with clustering boundary elements,it has some drawbacks,for instance,the original rough K-means clustering algorithm is sensitive to the initial center,the set-up of empirical weigh ignores data difference,the unreasonable threshold setting engenders fluctuation of clustering results.To tackle these drawbacks,this paper proposed an improved rough K-means clustering algorithm combined with ant colony algorithm.The improved algorithm is optimized for rough K-means clustering by using random probability selection strategy and pheromone update of positive and negative feedback mechanisms in ant colony algorithm,and using dynamic threshold adjustment algorithm and associated weights method.Finally,the UCI's Iris set,Balance-scale set and Wine set are used for verification of the algorithm.The results show that this algorithm exhibits a higher clustering accuracy.
作者
刘洋
王慧琴
张小红
Liu Yang;Wang Huiqin;Zhang Xiaohong(School of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an,710055,China;School of Management,Xi'an University of Architecture and Technology,Xi'an,710055,China;School of Communication and Information Engineering,Xi'an University of Science and Technology,Xi'an,710054,China)
出处
《数据采集与处理》
CSCD
北大核心
2019年第2期341-348,共8页
Journal of Data Acquisition and Processing
基金
教育部归国留学人员科研扶持基金(K05055)资助项目
教育部高等学校博士学科点专项科研基金
博导类联合(20126120110008)资助项目
关键词
聚类
K均值
蚁群算法
粗糙集
目标函数
cluster
K-means
ant colony algorithm
rough sets
objective function
