فهرست پایان نامه خوشه بندی فازی داده ها بر اساس منطق فازی
فهرست:
فصل اول: مقدمه
خوشهبندی ....................................................................................................................................... 2
خوشهبندی فازی ............................................................................................................................. 5
الگوریتمهای پایهای خوشهبندی فازی ............................................................................... 5
روش کار خوشهبندی فازی .................................................................................................... 9
مروری بر مقالات خوشهبندی فازی سالهای اخیر .......................................................... 8
خوشهبندی تفاضلی ........................................................................................................................ 11
ماشین بردار پشتیبان ................................................................................................................... 12
روش کار ماشین بردار پشتیبان ......................................................................................... 12
ماشین بردار پشتیبان جداییپذیر .................................................................................... 14
ماشین بردار پشتیبان غیرخطی ...................................................................................... 15
فصل دوم: مروری بر کارهای انجام شده
2-1 مقدمه ............................................................................................................................................ 19
2-2 کارهای انجام شده ...................................................................................................................... 19
فصل سوم: روش پیشنهادی
3-1 مقدمه ......................................................................................................................................... 24
3-2 چارچوب کلی روش پیشنهادی .............................................................................................. 24
فصل چهارم: نتایج شبیهسازی
4-1 مقدمه ............................................................................................................................................ 28
4-2 پایگاهداده و پارامترهای شبیهسازی ...................................................................................... 28
فصل پنجم: نتیجهگیری و کارهای آینده
5-1 تیجهگیری...............................................................................................................................................33
5-2 کارهای آینده.................................................................................................................................. 33
واژهنامه ......................................................................................................................................................... 34
مراجع ............................................................................................................................................................ 35
منبع:
[1] Osmar R. Zaïane: “Principles of Knowledge Discovery in Databases - Chapter 8: Data Clustering” .
[2] Pier Luca Lanzi: “Ingegneria della Conoscenza e Sistemi Esperti – Lezione 2: Apprendimento non supervisionato”.
[3] Grossman R.L. and Hornick M.F. and Meyer G., Data mining standards initiatives, Communications of the ACM, Vol 45, No 8, 2002.
[4] F. Hoppner, F.Klawonn, R.Kruse, T.Runkler; Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition, John Wiley & Sons, 2000.
[5] H.Timm, C.Borgelt, C.Do¨Ring,R.Andkruse, “An Extension To Possibilistic Fuzzy Cluster Analysis”, Fuzzy sets And Systems 147, 3–16, 2004.
[6] Chiu, S., "Fuzzy Model Identification Based on Cluster Estimation," Journal of Intelligent & Fuzzy Systems,Vol. 2, No. 3, Sept. 1994.
[7] R.P. Paiva, A. Dourado, Interpretability and learning in neuro-fuzzy systems, Fuzzy Sets Syst. 147 (1) 17–38.2004.
[8]J.C. Dunn; “A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well Separated Clusters”. Journ. Cybern. 3, 95-104, 1974.
[9]J. M. Geusebroek, G. J. Burghouts, and A.W. M.Smeulders. “The Amsterdam library of object images”, Int. J. Comput. Vision, 61(1):103–112, January 2005.
[10] C. Cortes, V. Vapnik, “Support-VectorNetworks”, Machine Learning, Vol. 20, pp. 273-297, 1995.
[11] C.J.C. Burges, “A Tutorial on Support VectorMachines for Pattern Recognition”, Data Miningand Knowledge Discovery, Vol. 2, pp. 121-167,1998.
[12] B. Schölkopf, A.J. Smola, Learning withKernels, MIT Press, Cambridge, MA, 2002.
[13] B. Schölkopf et al., “Comparing SupportVector Machines with Gaussian Kernels to RadialBasis Function Classifiers”, IEEE Trans. on SignalProcessing, Vol. 45, No. 11, pp. 2758-2765, Nov.1997.
[14] C.-F. Lin, S.-D. Wang, “Fuzzy Support VectorMachines”, IEEE Trans. on Neural Networks, Vol.13, No. 2, pp. 464-471, March 2002.
[15] S. Abe, T. Inoue, “Fuzzy Support VectorMachines for Multiclass Problems”, EuropeanSymposium on Artificial Neural Networks(ESANN’2002), pp. 113-118, Bruges, Belgium,April 2002.
[16] C. Juang, Member, IEEE, S. Chiu, and S. Shiu, “Fuzzy System Learned Through Fuzzy Clustering and Support Vector Machine for Human Skin Color Segmentation,” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 37, NO. 6, NOVEMBER 2007.
[17] S.Chen, Senior Member, IEEE, and Y. Chang “A New Method for Weighted Fuzzy Interpolative Reasoning Based on Weights-Learning Techniques” vol. 12, no. 12, pp. 820-832, 2004.
[18] E. I. Papageorgiou, Ath. Markinos, and Th. Gemtos, “Learning Algorithms for Fuzzy Cognitive” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 42, NO. 2, MARCH 2012.
[19] H.-P. Huang, Y.-H. Liu, “Fuzzy SupportVector Machines for Pattern Recognition and DataMining”, International Journal of Fuzzy Systems,Vol. 4, No. 3, pp. 826-835, Sep. 2002.
[20] J. Platt, “Fast training of support vector machines using sequential minimal optimization,” in Advances in Kernel Methods-Support Vector Learning, B. Schölkopf, C. Burges, and A. Smola, Eds. Cambridge,MA: MIT Press, 1999, pp.185–208.
[21] Ftp.ics.uci.edu/pub.