Title :
A clustering method for asymmetric proximity data based on bi-links with ε-indiscernibility
Author :
Hirano, Shoji ; Tsumoto, Shusaku
Author_Institution :
Dept. of Med. Inf., Shimane Univ., Izumo, Japan
Abstract :
In this paper, we propose a clustering method for non-metric proximity data based on the ε-indiscernibility. First, we introduce a hierarchical grouping method based on bi-links, which groups objects when bi-directional links are established between objects that have asymmetric dissimilarities. Next, we incorporate the concept of ε-indiscernibility into the process of establishing bi-directional links in order to allow users to control the level of asymmetry that can be ignored in merging a pair of objects. Experimental results on the soft drink brand switching data showed that this approach may have a possibility of producing better clusters compared to the straightforward use of bi-links.
Keywords :
data handling; pattern clustering; asymmetric dissimilarity; asymmetric proximity data; bidirectional link; clustering method; e-indiscernibility; hierarchical grouping method; nonmetric proximity data; soft drink brand switching data; Bidirectional control; Buildings; Clustering algorithms; Clustering methods; Correlation; Merging; Switches; ε-indiscernibility; asymmetric proximity; clustering;
Conference_Titel :
Granular Computing (GrC), 2011 IEEE International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-4577-0372-0
DOI :
10.1109/GRC.2011.6122601
