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

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Abstract

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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References

  • Jain, A.K., Murty, M.N., Flynn, P.J. (1999). Data clustering: a review. ACM Computing Surveys, 31(3), 264–323.

    Article  Google Scholar 

  • Romesburg, H.C. (1989). Cluster analysis for researchers. Malabar: Krieger.

    Google Scholar 

  • Saito, T., & Yadohisa, H. (2004). Data analysis of asymmetric structures: Advanced approaches in computational statistics. CRC Press.

  • Bass, F.M., Pessemier, E.A., Lehmann, D.R. (1972). An experimental study of relationships between attitudes, brand preference, and choice. Behavioral Science, 17(6), 532–541.

    Article  Google Scholar 

  • Hubert, L. (1973). Min and max hierarchical clustering using asymmetric similarity measures. Psychometrika, 38, 63–72.

    Article  MATH  Google Scholar 

  • Takeuchi, A., Saito, T., Yoshida, H. (2007). Asymmetric agglomerative hierarchical clustering algorithms and their evaluations. Journal of Classification, 24, 123–143.

    Article  MathSciNet  MATH  Google Scholar 

  • Hathaway, R.J., & Bezdek, J.C. (1994). NERF c-means: non-Euclidean relational fuzzy clustering. Pattern Recognition, 27(3), 429–437.

    Article  Google Scholar 

  • Sato-Ilic, M., & Jain, L.C. (2007). Asymmetric clustering based on self-similarity. In Proceedings of the third international conference on intelligent information hiding and multimedia signal processing (pp. 361–364).

  • Slowinski, R., & Vanderpooten, D. (1997). Similarity relation as a basis for rough approximations. Advances in Machine Intelligence & Soft-Computing, IV, 17–33.

    Google Scholar 

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Acknowledgement

This work was supported in part by the grant-in-aid for scientific research (C) #23500179, by the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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Authors and Affiliations

  1. Department of Medical Informatics, School of Medicine, Shimane University, 89-1 Enya-cho, Izumo, Shimane, 693-8501, Japan

    Shoji Hirano & Shusaku Tsumoto

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  1. Shoji Hirano
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  2. Shusaku Tsumoto
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Correspondence to Shoji Hirano.

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Hirano, S., Tsumoto, S. Clustering of non-metric proximity data based on bi-links with ϵ-indiscernibility. J Intell Inf Syst 41, 61–71 (2013). https://doi.org/10.1007/s10844-012-0218-3

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  • DOI: https://doi.org/10.1007/s10844-012-0218-3

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