Title
Classic: A hierarchical clustering algorithm based on asymmetric similarities
Abstract
The nearest neighbors relation (NNR) is defined in terms of a given asymmetric matrix of similarities of data items. This paper presents a new clustering algorithm, called CLASSIC, based on an iteratively defined nested sequence of NNRs. CLASSIC has been applied to various types of gestalt clustering problems. For CLASSIC applications in which asymmetric similarities are not available a priori, this paper also introduces a method for obtaining asymmetric similarities from Euclidean distances. This method has been used in the detection of gestalt clusters by CLASSIC.
Year
DOI
Venue
1983
10.1016/0031-3203(83)90023-7
Pattern Recognition
Keywords
Field
DocType
Clustering,Asymmetric measure,Similarity,Hierarchical clustering,Gestalt cluster,Classification,Computer,Pattern recognition
Hierarchical clustering,Canopy clustering algorithm,Fuzzy clustering,Pattern recognition,Correlation clustering,A priori and a posteriori,Gestalt psychology,Artificial intelligence,Cluster analysis,Mathematics,Machine learning,Single-linkage clustering
Journal
Volume
Issue
ISSN
16
2
0031-3203
Citations 
PageRank 
References 
6
1.06
10
Authors
1
Name
Order
Citations
PageRank
Kazumasa Ozawa1199.29