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

In this paper, we propose a hierarchical grouping method for non-metric proximity data based on bi-links and ϵ -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 wi...

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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:
Artificial Intelligence
Computer Science
Data Structures and Information Theory
Information Storage and Retrieval
IT in Business
Natural Language Processing (NLP)
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description In this paper, we propose a hierarchical grouping method for non-metric proximity data based on bi-links and ϵ -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 ϵ -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.
doi_str_mv 10.1007/s10844-012-0218-3
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subjects Artificial Intelligence
Computer Science
Data Structures and Information Theory
Information Storage and Retrieval
IT in Business
Natural Language Processing (NLP)
title Clustering of non-metric proximity data based on bi-links with ϵ-indiscernibility
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