Clustering of non-metric proximity data based on bi-links with -indiscernibility

Issue Title: Special issue on Data Mining In this paper, we propose a hierarchical grouping method for non-metric proximity data based on bi-links and -indiscernibility. It hierarchically forms directional links among objects according their directional proximities. A new cluster can be formed when...

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Published in:Journal of intelligent information systems 2013-08, Vol.41 (1), p.61-71
Main Authors: Hirano, Shoji, Tsumoto, Shusaku
Format: Article
Language:English
Subjects:
Analysis
Asymmetry
Clusters
Information systems
Intelligent systems
Links
Merging
Proximity
Soft drinks
Studies
Switching
Symmetry
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Tsumoto, Shusaku
description Issue Title: Special issue on Data Mining In this paper, we propose a hierarchical grouping method for non-metric proximity data based on bi-links and -indiscernibility. It hierarchically forms directional links among objects according their directional proximities. A new cluster can be formed when objects in two clusters are connected with bi-directional links (bi-links). The concept of -indiscernibility is incorporated into the process of establishing bi-links. This scheme enables 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 is capable of producing better clusters compared to the straightforward use of bi-links.[PUBLICATION ABSTRACT]
doi_str_mv 10.1007/s10844-012-0218-3
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subjects Analysis
Asymmetry
Clusters
Information systems
Intelligent systems
Links
Merging
Proximity
Soft drinks
Studies
Switching
Symmetry
title Clustering of non-metric proximity data based on bi-links with -indiscernibility
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