Clustering of non-metric proximity data based on bi-links with ϵ-indiscernibility
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
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.
- Jain, AK, Murty, MN, Flynn, PJ (1999) Data clustering: a review. ACM Computing Surveys 31: pp. 264-323 CrossRef
- Romesburg, HC (1989) Cluster analysis for researchers. Krieger, Malabar
- Saito, T., & Yadohisa, H. (2004). Data analysis of asymmetric structures: Advanced approaches in computational statistics. CRC Press.
- Bass, FM, Pessemier, EA, Lehmann, DR (1972) An experimental study of relationships between attitudes, brand preference, and choice. Behavioral Science 17: pp. 532-541 CrossRef
- Hubert, L (1973) Min and max hierarchical clustering using asymmetric similarity measures. Psychometrika 38: pp. 63-72 CrossRef
- Takeuchi, A, Saito, T, Yoshida, H (2007) Asymmetric agglomerative hierarchical clustering algorithms and their evaluations. Journal of Classification 24: pp. 123-143 CrossRef
- Hathaway, RJ, Bezdek, JC (1994) NERF c-means: non-Euclidean relational fuzzy clustering. Pattern Recognition 27: pp. 429-437 CrossRef
- 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: pp. 17-33
- 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
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Asymmetric proximity
- Industry Sectors