摘要
针对当数据集合中的数据属性差异不明显时,传统的均值聚类算法会收敛到局部最小值点,造成算法聚类结果不准、精度下降的问题,提出了一种基于密度的加权模糊均值聚类算法。该算法通过计算差异属性类中的相关密度,运用密度作为确定初始类中心的方法,得到了聚类效果更好的初始值。之后用加权模糊算法克服类划分中数据属性差异不明显带来的弊端,对类中差异属性进行归类划分。实验结果表明,该算法依然可以区分出不同属性的重要程度,而且其稳定性和聚类效果都有一定的提高。
The traditional clustering algorithm will converge to a local minimum point when the initial objects＇ attributes have no obvious difference,which can cause the decline of algorithms＇ accuracy and incorrectness of the results.In order to overcome these drawbacks,a density based weighted fuzzy c-mean clustering algorithm was proposed.It used the results of the calculation of the relative density differences attributes to determine the initial partition.After obtaining the better initial centers,a weighted fuzzy algorithm which can distinguish the importance of each attribute was implemented.Experimental results show that the algorithm not only can discriminate the attributes＇ contribution,but also can improve the stability and accuracy.
出处
《计算机科学》
CSCD
北大核心
2012年第5期180-182,共3页
Computer Science
基金
河南省重大科技攻关项目(102102210490)资助
关键词
聚类
模糊均值
属性加权
密度
误分类数
Clustering
Fuzzy C-means
Attribute weighted
Density
Number of misclassification
