• Publisher

Sǿrensen, T. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to the analyses of the vegetation on Danish commons. Biologiske Skrifter, 5(4), pp.1-34.
Sneath, P. (1957). The application of computers to taxonomy. Journal of General Microbiology, 17(1), pp.201-226.
Sokal, R. and Sneath, P. (1963). Principles of Numerical Taxonomy. San Francisco: W.W.Freeman.
Ward, J. (1963). Hierarchical grouping to optimize an objective function. Journal of the American
Statistical Association, 58(301), pp.236-244.
Anderberg, M.P. (1973) Cluster Analysis for Applications. New York: Academic Press.
Aldenderfer, M.S. and Blashfield, R.K. (1984). Cluster Analysis. Newbury Park: Sage University Paper.
Everitt, B.S. (1993). Cluster Analysis. Cambridge: Edward Arnold.
Sharma, S. (1996). Applied Multivariate Techniques. New York: Wiley.
Jain, A.K., Murty, M. N. and Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys, 31(3), pp. 264 – 323.
Yegar, R. R. (2000). Intelligent control of the hierarchical clustering process. IEEE Transactions
on Systems., Man and Cybenetics., 30, PART B(6), pp.835 – 845.
Forgey, E. W. (1965). Cluster Analysis of Multivariate Data: Efficiency Versus Interpretability of
Classifications, Biometrics, 21(3), pp.768-769.
Jancey, R. C. (1966). Multidimensional group analysis. Australian Journal of Botany, 14(1), pp. 127-130.
MacQueen, J. B. ((1967). Some methods of classification and analysis of multivariate
observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1 (AD 669871), pp.281-297, Berkeley: Univ. of California Press.
Ball, G. H. and Hall, D. J. (1965). ISODATA, A Novel Method of Data Analysis and Pattern
Classification. Menlo Park: Stanford Research Institute. (NTIS No. AD 699616).
Marriott, F. H. C. (1982). Optimization methods of cluster analysis. Biometrika, 69(2),
pp.417-421.
Selim, S. Z. and Ismail, M. A.. (1984). K-means type algorithms: a generalized convergence
theorem and characterization of local optimality. IEEE Trans. Pattern Analysis and
Machine Intelligence, 6(1), pp.81 – 87.
Dunn, J. C. (1974). A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters. Journal of Cybernetics, 3(3), pp.32-57.
Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithm. New York: Plenum Press.
Jain, A. K. and Dubes, R. C. (1988). Algorithms for clustering data. USA: Prentice-Hall.
Kamel, M. S. and Selim, S.Z. (1994). New algorithms for solving the fuzzy clustering problem. Pattern Recognition, 27(3), pp.421-428.
Jang, J. S. R., Sun, C. T. and Mizutani, E. (1996). Neuro-fuzzy and soft computing: a
computational approach to learning and machine intelligenc. Prentice-Hall.
Höppner, F., Klawonn, F., Kruse, R. and Runkler, T. Fuzzy cluster analysis. Chichester: Wiley & Sons, 1999.
Pawlak, Z. (1982). Rough sets. Int. Journal of Information and Computer Sciences, 11, pp.341–356.
Pawlak, Z. (1991). Rough sets, theoretical aspects of reasoning about data. Dortrecht: Kluwer Academic.
Skowron, A. and Rauszer, C. (1992). The discernibility matrices and functions in information
systems. In: R. Slowinski (Ed.), Intelligent decision support, handbook of applications and advances of the rough sets theory. Dortrecht: Kluwer Academic, pp. 331 – 362.
Komorowski, J., Pawlak, Z., Polkowski, L. and Skowron, A. (1998) Rough sets: a tutorial. In: Pal, S. and Skowron, A. (Eds.). Rough fuzzy hybridization: a new method for decision making. Berlin:Springer.
Dubois, D. and Prade, H. (1989). Rough fuzzy sets and fuzzy rough sets. Int. J. General Systems, 17, pp.191 – 209.
Bean, C. L. and Kambhampati, C. (July 2003). Knowledge-oriented clustering for decision support. Proc. IEEE International Joint Conference on Neural Networks. Portland, Oregon.
Okuzaki, T., Hirano, S., Kobashi, S., Hata, H. and Takahashi, Y. (Dec. 2002). A rough set based clustering method by knowledge combination. IEICE Transactions on Information and
Systems, E85 – D(12), pp.1898 – 1908.
Bean, C. L., Kambhampati, C. and Rajasekharan, S. (May 2002). A rough set solution to a fuzzy set problem. Proc. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Honolulu, Hawaii: World
Congress in Computational Intelligence.
Hirano, S. and Tsumoto, S. (2001). A knowledge-oriented clustering technique based on rough sets. In: Proc. 25th IEEE Int. Conference on Computer and Software Applications
(Compsac2001), Chicago, Illinois, pp. 632 – 637.
Manly, B. J. F. (2000). Multivariate statistical methods, a primer. New York: Chapman & Hall.
Hartigan, J. A. (1974) Clustering algorithms. New York: Wiley.