References (books articles, data, software, etc.)


{GOLD89} D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, 1989 (MAT 68-1989-48IN, CHI M.9, ING1 A.ELE.T.0264)

{ZIMM96} H.J. Zimmermann, Fuzzy set theory and its applications, 2ed., Kluwer Academic Publishers, 1996 (MAT 04-1996-01, MAT 04-1996-02, LETT 14.E.169)



  • {BEZ81} J. C. Bezdek,Pattern recognition with fuzzy objective function algorithms, Plenum Press, 1981 (CSB di Fisica CSB FIS577.26 BEZ 30; CSB di Ingegneria Secondo Polo 006.4 BEZ 2, DISEGD.0383, Secondo Polo 006.4 BEZ)
  • {BISH06} C. M. Bishop, Pattern recognition and machine learning, Springer, 2006 (CSB MAT 68-2006-05)
  • {DUDA73} R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons Inc, 1973 (CSB MAT 68-1973-03IN, CSB di Ingegneria Secondo Polo 006.4 DUD, CSB di Ingegneria DIPTEM Sez. Ing. Prod. F 1 / 0032)
  • {DHS01} R O. Duda, P. E. Hart, D. G. Stork, Pattern classification, 2nd ed., Wiley, 2001 (CSB di Ingegneria Secondo Polo 006.4 DUD 2001, Polo di Savona Polo di Savona-Ingegneria 006.4 DUD)
  • {KLIR95} G.J. Klir & B. Yuan, Fuzzy Sets and Fuzzy Logic - Theory and Applications, Prentice Hall, 1995 (ING-DIP  C 1 / 0032) (or also  G.J. Klir, T. A. Folger, Fuzzy sets uncertainty and information, Prentice Hall, 1988, MAT 68-1988-55IN).
  • {HAYK09} S. Haykin, Neural Networks and Learning Machines: A Comprehensive Foundation, Prentice Hall; 3rd rev. ed., 2009
  • {HERTZ91} J. A. Hertz , A. S. Krogh, R. G. Palmer, Introduction To The Theory Of Neural Computation, Westview Press, 1991
  • {JAIN88} A.K. Jain, R.C. Dubes. Algorithms for Clustering Data. Prentice-Hall, Inc., Upper Saddle River, NJ, 1988. (downloadable)
  • {KEC01} V. Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, Mit Press, 2001.
  • {LIN96} C.T. Lin, G. S. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall, 1996, ASIN: 0132351692  (ARC C.3613)


  • [DON00] D. L. Donoho, High-dimensional data analysis: The curses and blessings of dimensionality. Aide-Memoire of a Lecture at AMS Conference on Math Challenges of the 21st Century, 2000.
  • [EIS98] M. B. Eisen, P. T. Spellman, P. O. Brown,  D.  Botstein, Cluster analysis and display of genome-wide expression patterns, Proceedings of the National Academy of Science, Vol. 95, Issue 25, 14863-14868, 1998.
  • [FIL08] M. Filippone, F. Camastra, F. Masulli, S. Rovetta, "A survey of kernel and spectral methods for clustering", Pattern Recognition, 41, 1 pp. 176-190, 2008.
  • [FIL10] M. Filippone, F. Masulli, S. Rovetta, "Applying the Possibilistic C-Means Algorithm in Kernel-Induced Spaces", IEEE Transactions on Fuzzy Systems, 18/3, pp. 572-584, 2010.
  • [FUB07] Q. Fubin and D. Rui, “Simulated annealing for the 0/1 multidimensional knapsack problem,” Numer. Math. J. Chinese Univ., vol. 16, pp. 320–327, 2007.
  • [KRI09] H-P Kriegel Ludwig, P.  Kröger, A. Zimek, Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering, Journal ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 3, pp. 1-58,  2009 
  • [GUR98] P. Gurzi, A. Masulli, Spalvieri, M. L. Sotgiu, and G. Biella, Rough annealing by two-step clustering, with application to neuronal signals, Journal of Neuroscience Methods, vol 85(1), pp. 81-87, 1998.
  • [HON06] K. Honda, H. Ichihashi, A. Notsu, F. Masulli, S. Rovetta, "Several Formulations for Graded Possibilistic Approach to Fuzzy Clustering", in  Rough Sets and Current Trends in Computing, vol. LNCS 4259, pp. 939-948, Springer-Verlag, Heidelberg (Germany), 2006.
  • [LIN65] Lin, S. 1965, Bell System Technical Journal, vol. 44, pp. 2245–2269. 
  • [MASS06] A.M. Massone, L. Studer, F. Masulli, "Possibilistic Clustering Approach to Trackless Ring Pattern Recognition in RICH Counters", International Journal of Approximate Reasoning, 41/2, pp. 96-109, 2006. 
  • [MAS99]F. Masulli and A. Schenone, A fuzzy clustering based segmentation system as support to diagnosis in medical imaging, Artificial Intelligence in Medicine, vol. 16, pp. 129-147, 1999.
  • [MAS06] F. Masulli, S. Rovetta, Soft Transition from Probabilistic to Possibilistic Fuzzy Clustering'', IEEE Transactions on Fuzzy Systems, 14/4, pp. 516-526, 2006.
  • [MUR96] C.A. Murthy , N. Chowdhury, In search of optimal clusters using genetic algorithms, Pattern Recognition Letters 17 (1996) 825-832
  • [ROS90]K. Rose, E. Gurewitz and G.C. Fox, "A Deterministic Annealing Approach to Clustering," Pattern Recognition Letters, vol. 11, no.9, pp. 589-594, 1990.
  • [ROSE98] K. Rose, "Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems," Proceedings of the IEEE, vol. 80, pp. 2210-2239, 1998.
  • [ROV06] S. Rovetta, F. Masulli, Shared farthest neighbor approach to clustering of high dimensionality, low cardinality data, Pattern Recognition, 39, pp. 2415-2425, 2006
  • [SPIL95] R. Spillman, Solving large knapsack problems with a genetic algorithm, Intelligent Systems for the 21st Century., IEEE International Conference on Systems, Man and Cybernetics, vol 1 pp. 632--637, 1995 
  • [STE04] M. Steinbach, L. Ertoz, V. Kumar, Challenges of clustering high dimensional data, in Wille, L. T. (Ed.), Proceedings of New Directions in Statistical Physics - Econophysics, Bioinformatics, and Pattern Recognition, Springer Verlag, Berlin, pp. 273-307, 2004

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