[1] J. C. Bezdek, Pattern Recognition With Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.

[2] T. Calinski and J. Harabasz, A dendrite method for cluster analysis, Commun. Stat. 3 (1974), 1-27.

[3] C. M. Cuadras, J. Fortiana and F. Oliva, The proximity of an individual to a population with applications in discriminant analysis, J. Classification 14 (1997), 117-136.

[4] M. De C�ceres, F. Oliva and X. Font, On relational possibilistic clustering, Pattern Recognition (2006) (accepted for publication).

[5] J. C. Dunn, Indices of partition fuzziness and detection of clusters in large data sets, Fuzzy Automata and Decision Processes, Elsevier, New York, 1976.

[6] E. B. Fowlkes and C. L. Mallows, A method for comparing two hierarchical algorithms, J. Amer. Statist. Assoc. 78 (1983), 553-569.

[7] R. J. Hathaway, J. W. Davenport and J. C. Bezdek, Relational duals of the c-means clustering algorithms, Pattern Recognition 22 (1989), 205-212.

[8] L. Hubert and P. Arabie, Comparing partitions, J. Classification 2 (1985), 193-218.

[9] V. R. Iyer, M. B. Eisen, D. T. Ross, G. Schuler, T. Moore, J. C. F. Lee, J. M. Trent, L. M. Staudt, J. Hudson, M. S. Boguski, D. Lashkari, D. Shalon, D. Botstein and P. O. Brown, The transcriptional program in the response of human fibroblasts to serum, Science 283(5398) (1999), 83-87.

[10] A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Prentice Hall, Englewood Cliffs, New Jersey, 1988.

[11] R. Krishnapuram and J. M. Keller, A possibilistic approach to clustering, IEEE Trans. Fuzzy Systems 1 (1993), 98-110.

[12] R. Krishnapuram and J. M. Keller, The possibilistic c-means algorithm: Insights and recommendations, IEEE Trans. Fuzzy Systems 4 (1996), 385-393.

[13] P. Legendre and L. Legendre, Numerical Ecology. Developments in Environmental Modelling, 2nd ed., Elsevier, 1998.

[14] J. MacQueen, Some methods for classification and analysis of multivariate observation, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp. 281-297.

[15] W. M. Rand, Objective criteria for the evaluation of clustering methods, J. Amer. Statist. Assoc. 66 (1971), 846-850.

[16] P. J. Rousseuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math. 20 (1987), 53-65.

[17] P. H. A. Sneath and R. R. Sokal, Numerical Taxonomy. The Principles and Practice of Numerical Classification, W. H. Freeman, San Francisco, 1973.