A Comparison between Two Fuzzy Clustering Algorithms for Mixed Features
In this paper, a comparative analysis of the mixed-type variable fuzzy c-means (MVFCM) and the fuzzy c-means using dissimilarity functions (FCMD) algorithms is presented. Our analysis is focused in the dissimilarity function and the way of calculating the centers (or representative objects) in both algorithms.
KeywordsCluster Center Membership Degree Comparison Criterion Representative Object Mixed Feature
- 1.Schalkoff, R.J.: Pattern Recognition: Statistical, Structural and Neural approaches. John Wiley & Sons, Inc., USA (1992)Google Scholar
- 2.Yang, M.S., Hwang, P.Y., Chen, D.H.: Fuzzy Clustering algorithms for mixed feature variables. Fuzzy Sets and Systems (2003) (in Press)Google Scholar
- 3.Ayaquica, M.I., Martínez, T.J.F.: Fuzzy c-means algorithm to analyze mixed data. In: VI Iberamerican Symp. on Pattern Recognition, Florianopolis, Brazil, pp. 27–33 (2001)Google Scholar
- 4.Ayaquica, M.I.: Fuzzy c-means algorithm using dissimilarity functions. Thesis to obtain the Master Degree. Center for Computing Research, IPN, Mexico (2002) (in Spanish)Google Scholar