Clustering of non-metric proximity data based on bi-links with ϵ-indiscernibility
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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.
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- Clustering of non-metric proximity data based on bi-links with ϵ-indiscernibility
Journal of Intelligent Information Systems
Volume 41, Issue 1 , pp 61-71
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