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A clustering method for asymmetric proximity data based on bi-links with ε-indiscernibility

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.