Hierarchical Clustering of Non-Euclidean Relational Data Using Indiscernibility-Level

摘要：

In this paper,we present a clustering method for non Euclidean relational data based on the combination of indiscernibility level and linkage algorithm.Indiscernibility level quantifys the level of global agreement for classifying two objects into the same category as indiscernible objects.Single-linkage grouping is then used to merge objects according to the indiscernibility level from bottom to top and construct the dendrogram.This scheme enables users to examine the hierarchy of data granularity and obtain the set of indiscernible objects that meets the given level of granularity.Additionally,since indiscernibility level is derived based on the binary classifications assigned independently to each object,it can be applied to non-Euclidean,asymmetric relational data.

作者: Shoji Hirano Shusaku Tsumoto

作者单位: Department of Medical Informatics,Shimane University,School of Medicine 89-1 Enya-cho,Izumo,Shimane 693-8501,Japan

会议类型: 国际会议

会议名称: The Third International Conference on Rough Sets and Knowledge Tevhnology(RSKT 2008)(第三届粗糙集与知识技术国际会议)

会议地点: 成都

会议语种:英文

页码: 332-339

在线出版日期: 2008-05-17（万方平台首次上网日期，不代表论文的发表时间）