摘要
在数据挖掘中由于每个数据对象对于知识发现的作用是不同的,为了区分这些相异之处,给每个对象赋予一定量的值,因此在PAM聚类算法的基础上提出一种W-PAM(Weight Partitioning Around Medoids)聚类算法,它为簇中数据对象加入权重来提高算法的准确率,此外利用数据对象间的关联限制能够提高聚类算法的效果。探讨了一种W-PAM算法与关联限制相结合的限制聚类算法,该算法同时拥有W-PAM算法和关联限制的优点。实验结果证明,W-PAM的限制聚类算法可以更有效地利用所给的关联限制来改善聚类效果,提高算法的准确率。
In data mining,the effect of each data object on knowledge discovery is different.In order to distinguish these differences,this paper gave a certain amount of value to each object,and put forward a W-PAM(Weight Partitioning Around Medoids)clustering algorithm which is based on the PAM algorithm.It can improve the accuracy of the algorithm by adding weight to the data object in the cluster.Moreover,the effect of clustering algorithm can be improved by using the association among the data objects.In this paper,a W-PAM restricted clustering algorithm was proposed,which combines the W-PAM algorithm with the constraint clustering algorithm.The algorithm has advantages of the W-PAM restricted clustering algorithm and relevance constraints.The experimental results show that the W-PAM restricted clustering algorithm can effectively improve the clustering result and improve the accuracy of the algorithm.
出处
《计算机科学》
CSCD
北大核心
2016年第S2期447-450,共4页
Computer Science
基金
国家自然科学基金(61402241
61572260
61373017
61572261
61472192)
江苏省科技支撑计划(BE2015702)资助
关键词
数据挖掘
W-PAM
关联限制
限制聚类
Data mining
W-PAM
Association restriction
Restricted clustering
