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مقالات فارسیISIکنفرانسهاژورنالها

Interval Type-2 Fuzzy C-means clusteringBased on Particle Swarm Optimization

Publish place: 3rd International Conference on Recent Innovation in Industrial Engineering and Mechanical Engineering Conference
Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
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لینک ثابت به این Paper:
https://civilica.com/doc/594374

شناسه ملی سند علمی:

NRIME03_004

تاریخ نمایه سازی: 19 خرداد 1396

Abstract:

Interval type-2 FLSs are useful in circumstances where it is difficult to determine an exact numeric membership function, and there are measurement uncertainties. This paper proposes a hybrid fuzzy c-means clustering method with particle swarm optimization technique that is applicable for interval type-2 fuzzy data sets. A quantifying similarity measure based on Euclidean distances measurement between indices that represents the information contain in each interval type-2 fuzzy data is implemented to cluster data. Modified Xie-Beni index is proposed to measure the validity of interval type-2 fuzzy data clustering. The results show that the hybrid PSO-IT2FCM is superior and efficient method for interval type-2 fuzzy sets.

Keywords:

Fuzzy c-means clustering , interval type-2 fuzzy set , Particle Swarm Optimization , Xie-Beni validity index

Authors

Elahe Hajigol Yazdi

Department of Industrial Engineering, Yazd University, Yazd, Iran

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  • Bezdek, J., Fuzzy mathematics in pattern classification. Ph.D. thesis (1974), ...
  • Hisdal E., "The IF THEN ELSE statement and interval-values fuzzy ...
  • Hathway, R. J., & Bezdek, J., Optimization of clustering criteria ...
  • 8894e+6 5.9181 e+6 5.9344 e+6 5.8621 e+6 5.7854 e+6 ...
  • Kennedy, J., & Eberhart, R., Particle SWarm optimization. In Proceedings ...
  • Cheng, H. _ & Chen, J.-R., Automatically determine the membership ...
  • Chung.F , Rhee.H , Hwang.C, 2002, _ interval type-2 fuzzy ...
  • Dickerson, J. A, & Kosko, B., Fuzzy function approximation with ...
  • Klawonn F., Keller A..Fuzzy Clustering with Evolutionary Algorithms. Intelligent Systems ...
  • Karnik, N. N. and J. M. Mendel, "Introduction to Type-2 ...
  • Karnik, N. N. and J. M. Mendel, "Type-2 Fuzzy Logic ...
  • Karnik, N. N., Type-2 Fuzzy Logic Systems, Ph.D. Dissertation, University ...
  • Kennedy J., Eberhart R., Particle SWarm optimisation, vol. 4, in: ...
  • Krishna K., Murty, Genetic k-means algorithm, IEEE Transactions of System ...
  • John, R. I., "Fuzzy Sets of Type-2 J. of Advanced ...
  • John, R. I., "Type 2 Fuzzy Sets" Expert Update, Vol. ...
  • Karnik.N.N , Mendel..M, 1999, Type-2 Fuzzy Logic Systems", IEEE TRAN ...
  • Karnik, N. N. and J. M. Mendel, "Applications of Type-2 ...
  • Karnik, N. N. and J. M. Mendel, Applications of Type-2 ...
  • Karnik, N. N., J. M. Mendel and Q. Liang "Type-2 ...
  • Mualik U., B andyopadhyay S., Genetic al gorithm-based clustering technique, ...
  • Karnik, N. N. and J. M. Mendel, "Centroid of a ...
  • Karnik, N. N. and J. M. Mendel, "Operations on Type-2 ...
  • Ng M.K., Wong J.C., Clustering categorical data sets using Tabu ...
  • John, R. I. "Embedded Interval Valued Fuzzy Sets, " Proc. ...
  • Gaing Z.L, A particle SWarm optimization approach for optimum design ...
  • Kaufman, L., Rousseeuw, P.J., Finding groups in data: An introduction ...
  • Tan, P. N, Steinbach, M., & Kumar, V. (2005). Introduction ...
  • Armando L.F., Schmitz E.A., Lima P., Manso F.S.P., Optimized RFV ...
  • Feng, H.-M.Hybrid stages particle SWarm optimization learning fuzzy modeling systems ...
  • Ji J, Zhang N., Liu C., Zhong N., An ant ...
  • Zimmerman, R. D., Carlos, E. M.-S., & Gan, D., MATPOWER ...
  • Liu, X. W., On the maximum entropy parameterized interval approximation ...
  • Niknam T., An approach based on particle SWarm optimization for ...
  • Runkler, T. A., & Katz, C., Fuzzy clustering by particle ...
  • Yang, S. J. H., Context aware ubiquitous learning environments for ...
  • Fathian M., Amiri B., A honey-bee mating approach on clustering, ...
  • Hopgood, A. A. and Hirst, A. J., Keeping a distance ...
  • Li, L, Liu, X., & Xu, M., A novel fzzy ...
  • Niknam T, Olamaie J., Amiri B., A hybrid evolutionary algorithm ...
  • Niknam T., Bahmani Firouzi B., Nayeripour M., An efficient hybrid ...
  • Niknam T., Amiri B., Olamaie J., Arefi A., An efficient ...
  • Kao Y., Zahara E, A hybridized approach to data clustering, ...
  • Bleuler S. , Zimmermann P., Friberg M., and Zitzler E., ...
  • Cao, Krzysztof, GAKREM: a novel hybrid clustering algorithm, Information Sciences ...
  • Liu, H.C., Yih, J.M., Wu, D. B., & Liu, S.W., ...
  • Chen D., Zhao C., Data-driven fuzzy clustering based on maximum ...
  • Gan, G., Wu, J., & Yang, Z., A genetic fizzy ...
  • Yang, F., Sun, T., & Zhang, C., An efficient hybrid ...
  • Wang, W. P., Evaluating new product development performance by fizzy ...
  • Wang, T. C., & Lin, Y. L., Applying the consistent ...
  • Zhao, L, & Yang, Y., PSO-based single multiplicative neuron model ...
  • Mullen R.J., Monekosso D., Barman S., Remagnino P., A review ...
  • Niknam T, A new fuzzy adaptive hybrid particle SWarm optimization ...
  • Saeh, I., & Khairuddin, A. , Decision tree for static ...
  • Brouwer R., Groenwold A., Modified fuzzy c-means for ordinal valued ...
  • Zhang L, , GaoL..Shao X., Wen L., Zhi J., A ...
  • Izakian H., Abaraham A, Fuzzy C-means and fuzzy _ for ...
  • Kalyani S., Swarup K.S., Particle SWarm optimization based K-means clustering ...
  • Sacco W.F., Henderson N., Finding all solutions of nonlinear systems ...
  • Aliev R., Pedrycz W., Guirimov B., Aliev R., Ilhan U., ...
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