Cluster validation in linear fuzzy clustering of relational data from multi-cluster principal coordinate analysis view point
Cluster validation in linear fuzzy clustering of relational data from multi-cluster principal coordinate analysis view point
复制标题
从多簇主坐标分析角度对关系数据进行线性模糊聚类的聚类验证
DOI：
10.1109/fuzzy.2009.5277418
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
H. Ichihashi
中科院分区：
文献类型：
--
作者：
Naoki Haga;Katsuhiro Honda;A. Notsu;H. Ichihashi
本文考虑了一种新的方法，在线性模糊聚类的关系数据的聚类验证。考虑到线性模糊聚类与局部主成分分析之间的紧密联系，关系聚类模型可以看作是一个多类MDS模型。在新的聚类验证方法中，模糊划分的质量从多聚类主坐标分析的角度来衡量，其中每个聚类中重构的低维子结构与考虑模糊隶属度的主坐标分析结果进行比较.
This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluster MDS model. In the new cluster validation approach, the quality of fuzzy partitions is measured from the multi-cluster principal coordinate analysis view point, in which the reconstructed low dimensional substructure in each cluster is compared with the result of principal coordinate analysis considering fuzzy membership degrees to the cluster.