計劃人：楊敏生教授

服務單位：中原大學應用數學系

研究成果目錄

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期刊論文

．五年內著作

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1.  J. Hua, J. Yu and M.S. Yang, 2019, Fast clustering for signed graphs based on random walk gap, Social Networks, pp. 1-16 (In press) PDF

2.  M.S. Yang, S.J. Chang-Chien and Y. Nataliani, 2019, Unsupervised fuzzy model-based Gaussian clustering, Information Sciences, Vol. 481, pp 1–23. PDF

3.  Y. Nataliani and M.S. Yang, 2019, Powered Gaussian kernel spectral clustering, Neural Computing and Applications, Vol. 31, Supplement 1, pp 557–572. PDF

4.  M.S. Yang, S.J. Chang-Chien and Y. Nataliani, 2018, A fully-unsupervised possibilistic c-means clustering method, IEEE Access, Vol. 6, pp 78308–78320. PDF

5.  M.S. Yang and Z. Hussian, 2018, Fuzzy entropy for Pythagorean fuzzy sets with application to multicriterion decision making, Complexity, Vol. 2018, Article ID 2832839, pp. 1-14. PDF

6.  Z. Hussian and M.S. Yang, 2018, Entropy for hesitant fuzzy sets based on Hausdorff metric with construction of hesitant fuzzy TOPSIS, International Journal of Fuzzy Systems, Vol. 20, pp 2517–2533. PDF

7.  C.M. Hwang, M.S. Yang and W.L. Hung, 2018, New similarity measures of intuitionistic fuzzy sets based on the Jaccard index with its application to clustering, International Journal of Intelligent Systems, Vol. 33, pp 1672–1688. (SCI: 3.363) PDF

8.  M.S. Yang and Y. Nataliani, 2018, A feature-reduction fuzzy clustering algorithm with feature-weighted entropy, IEEE Transactions on Fuzzy Systems, Vol. 26, pp 817–835.  PDF

9.  M.S. Yang and C.C. Yeh, 2018, Fuzzy generalization and comparisons for the Rand index, International Journal of Intelligent Systems, Vol. 33, pp. 901–927. PDF

10.  J. Yu, C. Chaomurilige and M.S. Yang, 2018, On convergence and parameter selection of the EM and DA-EM algorithms for Gaussian mixtures, Pattern Recognition, Vol. 77, pp. 188–203 PDF

11.  M.S. Yang, S.J. Chang-Chien and W.L. Hung, 2017, Learning-based EM clustering for data on the unit hypersphere with application to exoplanet data. Applied Soft Computing, Vol. 60, pp. 101–114. PDF

12.  M.S. Yang, S.J. Chang-Chien and W.L. Hung, 2017, Learning-based EM clustering for data on the unit hypersphere with application to exoplanet data. Applied Soft Computing, Vol. 60, pp. 101–114. PDF

13.  M.S. Yang and Y. Nataliani, 2017, Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters. Pattern Recognition. Vol. 71, pp. 45–59.PDF

14.  Y. Nataliani and M.S. Yang, 2017, Spectral clustering for cell formation with minimum dissimilarities distance. (Accepted), Lecture Notes in Computer Science, vol. 10246, pp. 126-136. PDF

15.  C.C. Yeh and M.S. Yang, 2017, Evaluation measures for cluster ensembles based on a fuzzy generalized Rand index. Applied Soft Computing, Vol. 57, pp. 225–234. PDF

16.  C.M. Hwang and M.S. Yang, 2016, Belief and plausibility functions on intuitionistic fuzzy set. International Journal of Intelligent Systems, Vol. 31, pp. 556–568. PDF

17.  C.C. Yeh and M.S. Yang, 2016, A generalization of Rand and Jaccard indices with its fuzzy extension, International Journal of Fuzzy Systems, Vol. 18, pp. 1008–1018. PDF

18.  J.L. Hua, J. Yu and M.S. Yang, 2016, Correlative density-based clustering, Journal of Computational and Theoretical Nanoscience, Vol. 13, pp. 6935-6943. 

19.  M.S. Yang, Shou-Jen Chang-Chien, Wen-Liang Hung, 2016, An unsupervised clustering algorithm for data on the unit hypersphere, Applied Soft Computing, Vol. 42, pp. 290–313. PDF

20.  K.P. Lu, S.T Chang and  M.S. Yang, 2016, Change-point detection for shifts in control charts using fuzzy shift change-point algorithms. Computers and Industrial Engineering, Vol. 93, pp. 12−27. PDF

21.  S.T Chang, K.P. Lu and M.S. Yang, 2016, Stepwise possibilistic c-regressions, Information Sciences, Vol.334-335, pp. 307-322. PDF

22.  Chaomurilige, J. Yu, and M.S. Yang, 2015, Analysis of parameter selection for Gustafson-Kessel fuzzy clustering using Jacobian matrix. IEEE Transactions on Fuzzy Systems, Vol. 23, No. 6, pp.2329−2342. PDF

23.  S.T Chang, K.P. Lu and M.S. Yang, 2015, Fuzzy Change-Point Algorithms for Regression Models. IEEE Transactions on Fuzzy Systems, Vol. 23, No. 6, pp. 2343−2357. PDF

24.  W.L. Hung, S.J. Chang-Chien and M.S. Yang, 2015, An intuitive clustering algorithm for spherical data with application to extrasolar planets, Journal of Applied Statistics, Vol. 42, pp. 2220-2232. PDF

25.  M.S. Yang and C.N. Wang, 2015, Clustering methods based on weighted quasi-arithmetic means of T-transitive fuzzy relations, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Vol. 23, pp. 715−733. PDF

26.  C.N. Wang and M.S. Yang, 2015, Cluster analysis based on T-transitive interval-valued fuzzy relations, International Journal of Intelligent Systems, Vol. 30, pp. 1083−1100. PDF

27.  M.S. Yang, Y.J. Chen and Y. Nataliani, 2015, Bias-correction fuzzy c-regression algorithms, Lecture Notes in Computer Science, vol. LNAI 9119, pp. 283–293. PDF

28.  Y. Nataliani, C.M. Hwang and M.S. Yang, 2015, An exponential-type entropy measure on intuitionistic fuzzy sets, Lecture Notes in Computer Science, vol. LNAI 9119, pp. 218–227. 

29.  M.S. Yang and Y.C. Tian, 2015, Bias-correction fuzzy clustering algorithms, Information Sciences, Vol. 309, pp. 138−162. PDF

30.  M.S. Yang, Y.C. Tian and C.Y. Lin, 2014, Robust fuzzy classification maximum likelihood clustering with multivariate t-distributions, International Journal of Fuzzy Systems, Vol. 16, No. 4, pp. 566−576. PDF

31.  C.M. Hwang and M.S. Yang, 2014, New similarity measures between generalized trapezoidal fuzzy numbers using the Jaccard index, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Vol. 22, No. 6, pp. 831−844. PDF

32.  M.S. Yang, S.J. Chang-Chien, H.C. Kuo, 2014, On Mean Shift Clustering for Directional Data on a Hypersphere, Lecture Notes in Computer Science, vol. LNAI 8468, pp. 809-818. 

33.  H.C. Kuo, M.S. Yang, J.H. Yang, Y.C. Chen, 2014, SCM-Driven Tree View for Microarray Data, Lecture Notes in Computer Science, vol. LNAI 8468, pp. 207-215. 

34.  W.L. Hung and M.S. Yang, 2013, A Penalized Fuzzy Clustering Algorithm with its Application in Magnetic Resonance Image Segmentation, pp. 215-232, Book chapter, Chapter 14, In: Medical Applications of Artificial Intelligence, Arvin Agah (Ed.), CRC Press, Taylor & Francis, Boca Raton, Florida.

35.  C.M. Hwang and M.S. Yang, 2013, New construction for similarity measures between intuitionistic fuzzy sets based on lower, upper and middle fuzzy sets, International Journal of Fuzzy Systems, Vol. 15, No. 3, pp. 359−366. PDF

36.  M.S. Yang, C.Y. Lin and Y.C. Tian, 2013, A robust fuzzy classification maximum likelihood clustering framework, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Vol. 21, No. 5, pp. 755−776. PDF

37.  J. Yu, M.S. Yang and P. Hao, 2013, Clustering construction on a multimodal probability model, Information Sciences, Vol. 237, pp. 211–220. PDF

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．其他年份之著作

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38.  M.S. Yang, C.Y. Lai and C.Y. Lin, 2012, A robust EM clustering algorithm for Gaussian mixture models, Pattern Recognition, Vol. 45, No. 11, pp. 3950-3961. PDF

39.  C.M. Hwang and M.S. Yang, 2012, Modified cosine similarity measure between intuitionistic fuzzy sets, Lecture Notes in Computer Science, vol. LNAI 7530, pp. 285-293. PDF

40.  H.S. Tsai, W.L. Hung and M.S. Yang, 2012, A robust kernel-based fuzzy c-means algorithm by incorporating suppressed and magnified membership for MRI image segmentation, Lecture Notes in Computer Science, vol. LNAI 7530, pp. 744-754.

41.  W.L. Hung, S.J. Chang-Chien and M.S. Yang, 2012, Self-updating clustering algorithm for estimating the parameters in mixtures of von Mises distributions, Journal of Applied Statistics, Vol. 39, No. 10, pp. 2259-2274. 

42.  M.S. Yang, W.L. Hung and D.H. Chen, 2012, Self-organizing map for symbolic data, Fuzzy Sets and Systems, Vol. 203, No. 1, pp. 49-73. PDF

43.  S.J. Chang-Chien, W.L. Hung and M.S. Yang, 2012, On mean shift-based clustering for circular data, Soft Computing, Vol. 16, No. 6, pp. 1043-1060. PDF

44.  C.M. Hwang, M.S. Yang and W.L. Hung, 2012, On similarity, inclusion measure and entropy between type-2 fuzzy sets, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Vol. 20, No. 3, pp. 433-449. 

45.  C.M. Hwang, M.S. Yang, W.L. Hung and M.G. Lee, 2012, A similarity measure of intuitionistic fuzzy sets based on Sugeno integral with its application to pattern recognition, Information Sciences, Vol. 189, No. 1, pp. 93-109. PDF

46.  J. Yu, M.S. Yang and P. Hao, 2011, A novel multimodal probability model for cluster analysis, Fundamenta Informaticae, Vol. 111, No. 1, pp. 81-90. 

47.  J. Yu, M.S. Yang and E.S. Lee, 2011, Sample-weighted clustering methods, Computers and Mathematics with Applications, Vol. 62, No. 5, pp. 2200-2208. PDF

48.  W.L. Hung, D.H. Chen and M.S. Yang, 2011, Suppressed fuzzy-soft learning vector quantization for MRI segmentation, Artificial Intelligence in Medicine, Vol. 52, No. 1, pp. 33-43. PDF

49.  C.M. Hwang, M.S. Yang, W.L. Hung and E.S. Lee, 2011, Similarity, inclusion and entropy measures between type-2 fuzzy sets based on the Sugeno integral, Mathematical and Computer Modelling, Vol. 53, No. 9, pp. 1788-1797. PDF

50.  W.L. Hung, M.S. Yang and E.S. Lee, 2011, Cell formation using fuzzy relational clustering algorithm, Mathematical and Computer Modelling, Vol. 53, no. 9, pp.1776-1787. 

51.  C.M. Hwang and M.S. Yang, 2011, On fuzzy renewal processes for fuzzy random variables and extended theorems, International Journal of Intelligent Systems, Vol. 26, no. 2, pp. 115-128. 

52.  C.Y. Lai and M.S. Yang, 2011, Entropy-type classification maximum likelihood algorithms for mixture models, Soft Computing, Vol. 15, no. 2, pp. 373-381. PDF

53.  M.S. Yang and C.Y Lai, 2011, A robust automatic merging possibilistic clustering method, IEEE Transactions on Fuzzy Systems, Vol. 19 , No. 1, pp. 26-41. PDF

54.  M.S. Yang, K.L. Wu, K.C.R. Lin, H.C. Liu and J.F. Lirng, 2010, On three types of competitive learning algorithms with their comparisons and applications to MRI segmentation, International Journal of Intelligent Systems, Vol. 25, No. 11, pp. 1081-1102. 

55.  W.L. Hung, S.J. Chang-Chien and M.S. Yang, 2012, Self-updating clustering algorithm for estimating the parameters in mixtures of von Mises distributions, Journal of Applied Statistics. (Accepted) 

56.  M.S. Yang, C.Y. Lai and C.Y. Lin, 2012, A robust EM clustering algorithm for Gaussian mixture models, Pattern Recognition, Vol. 45, No. 11, pp. 3950-3961. PDF

57.  M.S. Yang, W.L. Hung and D.H. Chen, 2012, Self-organizing map for symbolic data, Fuzzy Sets and Systems, Vol. 203, No. 1, pp. 49-73. PDF

58.  C.M. Hwang, M.S. Yang and W.L. Hung, 2012, On similarity, inclusion measure and entropy between type-2 fuzzy sets, International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Vol. 20, No. 3, pp. 433-449. 

59.  S.J. Chang-Chien, W.L. Hung and M.S. Yang, 2012, On mean shift-based clustering for circular data. Soft Computing, Vol. 16, No. 6, pp. 1043-1060. PDF

60.  C.M. Hwang, M.S. Yang, W.L. Hung and M.G. Lee, 2012, A similarity measure of intuitionistic fuzzy sets based on Sugeno integral with its application to pattern recognition, Information Sciences, Vol. 189, No. 1, pp. 93-109. PDF

61.  J. Yu, M.S. Yang and P. Hao, 2011, A novel multimodal probability model for cluster analysis, Fundamenta Informaticae, Vol. 111, No. 1, pp. 81-90. 

62.  W.L.Hung, M.S. Yang, J. Yu and C.M. Hwang, 2011, Feature-weighted mountain method with its applications to color image segmentation, International Journal of Computational Intelligence Systems, Vol. 4, No. 5, pp. 1002-1011. 

63.  J. Yu, M.S. Yang and E.S. Lee, 2011, Sample-weighted clustering methods, Computers and Mathematics with Applications, Vol. 62, No. 5, pp. 2200-2208. PDF

64.  W.L. Hung, D.H. Chen and M.S. Yang, 2011, Suppressed fuzzy-soft learning vector quantization for MRI segmentation, Artificial Intelligence in Medicine, Vol. 52, No. 1, pp. 33-43. PDF

65.  C.M. Hwang, M.S. Yang, W.L. Hung and E.S. Lee, 2011, Similarity, inclusion and entropy measures between type-2 fuzzy sets based on the Sugeno integral. Mathematical and Computer Modelling, Vol. 53, No. 9, pp. 1788-1797. PDF

66.  W.L. Hung, M.S. Yang, E.S. Lee, 2011, Cell formation using fuzzy relational clustering algorithm. Mathematical and Computer Modelling, Vol. 53, no. 9, pp.1776-1787. 

67.  C.M. Hwang and M.S. Yang, 2011, On fuzzy renewal processes for fuzzy random variables and extended theorems, International Journal of Intelligent Systems, vol. 26, no. 2, pp. 115–128. 

68.  M.S. Yang and C.Y. Lai, 2011, A robust automatic merging possibilistic clustering method, IEEE Trans. Fuzzy Systems, vol. 19, no. 1, pp. 26–41. PDF

69.  C.Y. Lai and M.S. Yang, 2011, Entropy-type classification maximum likelihood algorithms for mixture models, Soft Computing. PDF

70.  W.L. Hung, M.S. Yang, J. Yu and C.M. Hwang, 2010, Feature-weighted mountain method with its applications to color image segmentation, Lecture Notes in Computer Science, vol. 6401, pp. 537-544.

71.  M.S. Yang, K.L. Wu, K.C.R. Lin, H.C. Liu and J.F. Lirng, 2010, On three types of competitive learning algorithms with their comparisons and applications to MRI segmentation, International Journal of Intelligent Systems, vol. 25, no. 11, pp. 1081-1102. 

72.  W.L. Hung, M.S. Yang and E.S. Lee, 2010, A robust clustering procedure for fuzzy data, Computers and Mathematics with Applications, vol. 60, no. 1, pp. 151-165.

73.  K.L. Wu, M.S. Yang and J.N. Hsieh, 2010, Mountain c-regressions method, Pattern Recognition, vol. 43, no. 1, pp. 86-98. PDF

74.  K.L. Wu, M.S. Yang and J.N. Hsieh, 2009, Robust cluster validity indexes, Pattern Recognition, vol. 42, no. 11, pp. 2541-2550. PDF

75.  J. Yu, M.S. Yang and P. Hao, 2009, A novel multimodal probability model for cluster analysis, Lecture Notes in Computer Science, vol. LNCS 5589, pp. 397-404. 

76.  R.W. Po, Y.Y. Guh and M.S. Yang, 2009, A new clustering approach using data envelopment analysis, European Journal of Operational Research, vol. 199, no. 1, pp. 276-284. PDF

77.  M.S. Yang and D.C. Lin, 2009, On similarity and inclusion measures between type-2 fuzzy sets with an application to clustering, Computers and Mathematics with Applications, vol. 57, no. 6, pp. 896-907. PDF

78.  Y.Y. Guh, M.S. Yang, R.W. Po and E.S. Lee, 2009, Interval-valued fuzzy relation-based clustering with its application to performance evaluation, Computers and Mathematics with Applications, vol. 57, no. 5, pp. 841-849. 

79.  C.M. Hwang and M.S. Yang, 2008, On entropy of fuzzy sets, Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 16, no. 4, pp. 519-527. PDF

80.  M.S. Yang and H.S. Tsai, 2008, A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction, Pattern Recognition Letters, vol. 29, no. 12, pp. 1713-1725. PDF

81.  Y.Y. Guh, M.S. Yang, R.W. Po and E.S. Lee, 2008, Establishing performance evaluation structures by fuzzy relation-based cluster analysis, Computers and Mathematics with Applications, vol. 56, no. 2, pp. 572-582. 

82.  M.S. Yang, K.L. Wu, J.N. Hsieh and J. Yu, 2008, Alpha-cut implemented fuzzy clustering algorithms and switching regressions, IEEE Trans. on Systems, Man, and Cybernetics-Part B, vol. 38, no. 3, pp. 588-603. PDF

83.  W.L. Hung, M.S. Yang and D.H. Chen, 2008, Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation, Pattern Recognition Letters, vol. 29, no. 9, pp. 1317-1325. 

84.  W.L. Hung and M.S. Yang, 2008, On similarity measures between intuitionistic fuzzy sets. International Journal of Intelligent Systems, vol. 23, no. 3, pp. 364-383. 

85.  M.S. Yang and J.H. Yang, 2008, Machine-part cell formation in group technology using a modified ART1 method, European Journal of Operational Research, vol. 188, no. 1, pp. 140-152. PDF

86.  W.L. Hung and M.S. Yang, 2008, On the j-divergence of intuitionistic fuzzy sets with its application to pattern recognition, Information Sciences, vol. 178, no. 6, pp. 1641-1650. PDF

87.  M.S. Yang, Y.H. Chiang, C.C. Chen and C.Y. Lai, 2008, A fuzzy k-partitions model for categorical data and its comparison to the GoM model, Fuzzy Sets and Systems, vol. 159, no. 4, pp. 390-405. 

88.  J. Yu and M.S. Yang, 2007, A generalized fuzzy clustering regularization model with optimality tests and model complexity analysis, IEEE Trans. on Fuzzy Systems, vol. 15, no. 5, pp. 904-915. PDF

89.  C.M. Hwang and M.S. Yang, 2007, Generalization of belief and plausibility functions to fuzzy sets based on Sugeno integral, International Journal of Intelligent Systems, vol. 22, no. 11, pp. 1215-1228. 

90.  W.L. Hung and M.S. Yang, 2007, Similarity measures of intuitionistic fuzzy sets based on Lp metric, Int. J. of Approximate Reasoning, vol. 46, no. 1, pp. 120-136.

91.  K.L. Wu and M.S. Yang, 2007, Mean shift-based clustering. Pattern Recognition, vol. 40, no. 11, pp. 3035-3052. PDF

92.  M.S. Yang, K.C.R. Lin, H.C. Lin and J.F. Lirng, 2007, Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms, Magnetic Resonance Imaging, vol. 25, no. 2, pp. 265-277. 

93.  M.S. Yang, W.L. Hung and C.H. Chang, 2006, A penalized fuzzy clustering algorithm, WSEAS Trans. on Computers Research, vol. 1, no. 2, pp. 83-88. 

94.  W.L. Hung and M.S. Yang, 2006, An omission approach for detecting outliers in fuzzy regression models, Fuzzy Sets and Systems, vol. 157, no. 23, pp. 3109-3122. 

95.  M.S. Yang, W.L. Hung and T.I. Chung, 2006, Alternative fuzzy c-means clustering algorithms with L1-norm and covariance matrix, Lecture Notes in Computer Science, vol. LNCS 4179, pp. 654-665. 

96.  I.G. Jiang, L.C. Yeh, W.L. Hung and M.S. Yang, 2006, Data analysis on the extra-solar planets using robust clustering, Monthly Notices of the Royal Astronomical Society, vol. 370, no. 3, pp. 1379-1392. 

97.  M.S. Yang, W.L. Hung and F.J. Cheng, 2006, Mixed-variable fuzzy clustering approach to part family and machine cell formation for GT applications, International Journal of Production Economics, vol. 103, no. 1, pp. 185-198. 

98.  W.L. Hung and M.S. Yang, 2006, Fuzzy entropy on intuitionistic fuzzy sets, International Journal of Intelligent Systems, vol. 21, no. 4, pp. 443-451.

99.  W.L. Hung, M.S. Yang and D.H. Chen, 2006, Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation, Pattern Recognition Letters, vol. 27, no. 5, pp. 424-438. 

100.  K.L. Wu and M.S. Yang, 2006, Alternative learning vector quantization, Pattern Recognition, vol. 39, no. 3, pp. 351-362. 

101.  M.S. Yang and K.L. Wu, 2006, Unsupervised possibilistic clustering, Pattern Recognition, vol. 39, no. 1, pp. 5-21.  PDF

102.  M.S. Yang and K.L. Wu, 2005, A modified mountain clustering algorithm, Pattern Analysis and Applications, vol. 8, pp. 125-138.  PDF

103.  M.S. Yang, W.L. Hung and S.J. Chang-Chien, 2005, On a similarity measure between LR-type fuzzy numbers and its application to database acquisition, International Journal of Intelligent Systems, vol. 20, no. 10, pp. 1001-1016.

104.  M.S. Yang and H.S. Tsai, 2005, An alternative fuzzy compactness and separation clustering algorithm, Lecture Notes in Computer Science, vol. LNCS 3708, pp. 146-153. 

105.  J. Yu and M.S. Yang, 2005, A note on the ICS algorithm with corrections and theoretical analysis, IEEE Trans. on Image Processing, vol. 14, no. 7, pp. 973-978.  

106.  M.S. Yang and C.Y. Lai, 2005, Mixture Poisson regression models for heterogeneous count data based on latent and fuzzy class analysis, Soft Computing, vol. 9, no. 7, pp.519-524. 

107.  K.L. Wu and M.S. Yang, 2005, A cluster validity index for fuzzy clustering, Pattern Recognition Letters, vol. 26, no. 9, pp. 1275-1291.  PDF

108.  K.L. Wu, J. Yu and M.S. Yang, 2005, A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests, Pattern Recognition Letters, vol.26, no. 5, pp. 639-652. 

109.  J.H. Yang and M.S. Yang, 2005, A control chart pattern recognition system using a statistical correlation coefficient method, Computers & Industrial Engineering, vol. 48, no. 2, pp. 205-221.  PDF

110.  J. Yu and M.S. Yang, 2005, Optimality test for generalized FCM and its application to parameter selection, IEEE Trans. on Fuzzy Systems, vol. 13, no. 1, pp. 164-176. PDF

111.  W.L. Hung and M.S. Yang, 2005, Fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation, Fuzzy Sets and Systems, vol. 150, no. 3, pp. 561-577. 

112.  M.S. Yang and N.Y. Yu, 2005, Estimation of parameters in latent class models using fuzzy clustering algorithms, European Journal of Operational Research, vol. 160, no. 2, pp. 515-531.  PDF

113.  W.L. Hung and M.S. Yang, 2004, Similarity measures between type-2 fuzzy sets, Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 12, no. 6, pp. 827-841. 

114.  M.S. Yang and H.M. Chen, 2004, Fuzzy class logistic regression analysis, Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 12, no. 6, pp. 761-780. 

115.  W.L. Hung and M.S. Yang, 2004, Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance, Pattern Recognition Letters, vol. 25, no. 14, pp. 1603-1611.  PDF

116.  M.S. Yang and K. L. Wu, 2004, A similarity-based robust clustering method, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26, no. 4, pp. 434-448. PDF

117.  Y.Y. Hong, Z.T. Chao and M.S. Yang, 2004, A fuzzy multiple linear regression based loss formula in electric distribution systems, Fuzzy Sets and Systems, vol. 142, no. 2, pp. 293-306. 

118.  M.S. Yang, P. Y. Hwang and D.H. Chen, 2004, Fuzzy clustering algorithms for mixed feature variables, Fuzzy Sets and Systems, vol. 141, no. 2, pp. 301-317.  PDF

119.  M.S. Yang and H.H. Liu, 2003, Fuzzy least-square algorithms for interactive fuzzy linear regression models, Fuzzy Sets and Systems, vol. 135, no. 2, pp. 305-316l.  PDF

120.  M.S. Yang, T.C. Chen and K.L. Wu, 2003, Generalized belief function, plausibility function, and Dempster's combinational rule to fuzzy sets, International Journal of Intelligent Systems, vol. 18, no. 8, pp. 925-937. 

121.  J. Yu and M.S. Yang, 2003, A study on a generalized FCM, Lecture Notes in Artificial Intelligence, vol. 2639, pp. 390-393.

122.  K.C.R. Lin, M.S. Yang, H.C. Liu, J.F. Lirng and P.N. Wang, 2003, Generalized Kohonen's competitive learning algorithms for ophthalmological MR image segmentation, Magnetic Resonance Imaging, vol. 21, no. 8, pp. 863-870. 

123.  K.L. Wu and M.S. Yang, 2003, A fuzzy-soft learning vector quantization, Neurocomputing, vol. 55, no. 10, pp. 681-697. 

124.  M.S. Yang and T.S. Lin, 2002, Fuzzy least-square linear regression analysis for fuzzy input-output data, Fuzzy Sets and Systems, vol. 126, no. 3, pp. 389-399. 

125.  M.S. Yang, Y.J. Hu, K.C.R. Lin and C.C.L Lin, 2002, Segmentation techniques for tissue differentiation in MRI of Ophthalmology using fuzzy clustering algorithms, Magnetic Resonance Imaging, vol. 20, no. 2, pp. 173-179.  PDF

126.  K.L. Wu and M.S. Yang, 2002, Alternative c-means clustering algorithms, Pattern Recognition, vol. 35, no. 10, pp. 2267-2278. PDF

127.  M.S. Yang and J.H. Yang, 2002, A fuzzy-soft learning vector quantization for control chart pattern recognition, International Journal of Production Research, vol. 40, no. 12, pp. 2721-2731. PDF

128.  M.S. Yang and H.M. Shih, 2001, Cluster analysis based on fuzzy relations, Fuzzy Sets and Systems, vol. 120, no. 2, pp. 197-212. 

129.  M.S. Yang and H.H. Liu, 1999, Fuzzy clustering procedures for conical fuzzy vector data, Fuzzy Sets and Systems, vol. 106, pp. 189-200. 

130.  M.S. Yang and C.H. Chen, 1998, On the edited fuzzy k-nearest neighbor rule, IEEE Trans. on Systems, Man and Cybernetics-Part B, vol. 28, pp. 461-466.  PDF

131.  M.S. Yang and M.C. Liu, 1998, On possibility analysis of fuzzy data, Fuzzy Sets and Systems, vol. 94, pp. 171-183. 

132.  M.S. Yang and C.H. Ko, 1997, On cluster-wise fuzzy regression analysis, IEEE Trans. On Systems, Man, and Cybernetics-Part B, vol. 27, pp. 1-13.  PDF

133.  M.S. Yang and J.A. Pan, 1997, On fuzzy clustering of directional data, Fuzzy Sets and Systems, vol. 91, pp. 319-326. 

134.  M.S. Yang and C.H. Ko, 1996, On a class of fuzzy c-numbers clustering procedures for fuzzy data, Fuzzy Sets and Systems, vol. 84, pp. 49-60. PDF

135.  M.S. Yang, 1994, On asymptotic normality of a class of fuzzy c-means clustering procedures, International Journal of General Systems, vol. 22, pp. 391-403. 

136.  M.S. Yang and C.F. Su, 1994, On parameter estimation for the normal mixtures based on fuzzy clustering algorithms, Fuzzy Sets and Systems, vol. 68, pp. 13-28. 

137.  M.S. Yang and C.T. Chen, 1994, Convergence rate of the fuzzy generalized nearest neighbor rule, Computers and Mathematics with Application, vol. 27, pp.1-8. 

138.  M.S. Yang and C.T. Chen, 1993, On strong consistency of the fuzzy generalized nearest neighbor rule, Fuzzy Sets and Systems, vol. 60, pp. 273-281. 

139.  M.S. Yang, 1993, A survey of fuzzy clustering, Mathematical and Computer Modeling, vol. 18, pp. 1-16. 

140.  M.S. Yang, 1993, Convergence properties of the generalized fuzzy c-means clustering algorithms, Computers and Mathematics with Applications, vol. 25, pp. 3-11. 

141.  M.S. Yang, 1993, On a class of fuzzy classification maximum likelihood procedures, Fuzzy Sets and Systems, vol. 57, pp. 365-375. PDF

142.  M.S. Yang and K.F. Yu, 1992, On existence and strong consistency of a class of fuzzy c-means clustering procedures, Cybernetics and Systems: An International Journal, vol. 23, pp. 583-602.

143.  M.S. Yang and K.F. Yu, 1990, On stochastic convergence theorems for the fuzzy c-means clustering procedure, Int. J. of General Systems, vol. 16, pp. 397-411.

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研討會論文

1.      W. Weku and M.S. Yang, 2016, Robust GK fuzzy clustering algorithms with cluster core and analysis on parameter selection, Proceedings of the 2016 Asian Mathematical Conference (AMC 2016), Bali, Indonesia, July 25-29, 2016, p. 480 (S09-CT-76).

2.      M.S. Yang, 2016, Applications of fuzzy clustering in regression models, The 6th China Conference on Data Mining (CCDM 2016), Guilin, Guangxi, China, 20-22 May 2016. (Invited speaker)

3.      M.S. Yang and R.J. Wong, 2016, A learning-based EM algorithm for t-distribution mixtures, Proceedings of 2016 Global Conference on Engineering and Applied Science (GCEAS 2016), Hokkaido, Japan, July 19-21, 2016, pp. 393-404.

4.      M.S. Yang, 2015, On fuzzy change-point algorithms for regression models, Proceedings of the 20th International Conference on Applied Mathematics (AMATH'15), Budapest, Hungary, December 12-14, 2015, p. 11. (Invited plenary speaker)

5.      Y.C. Tian and M.S. Yang, 2015, A learning-based EM mixture regression algorithm, Proceedings of the 17th International Conference on Modelling, Optimization and Simulation (ICMOS 2015), Prague, Czech, 9-10 July 2015, 17 (7) Part II, pp. 351-354.

6.      M.S. Yang, 2014, On maximum likelihood clustering via multimodal probability model, Proceedings of the 8th International Conference on Applied Mathematics, Simulation, Modelling (ASM’14), Florence, Italy, 22-24 November 2014, p. 16. (Invited plenary speaker)

7.      M.S. Yang, C.Y. Lin and Y.C. Tian, 2014, Fuzzy classification maximum likelihood clustering with t-distributions, Proceedings of the 4th International Conference on Mechanics, Simulation and Control (ICMSC2014), Moscow, Russia, 20-23 June 2014, pp. 392-397.

8.      M.S. Yang, 2014, Exponential-type Robust Clustering and its Applications to Interval Data, 2014 Workshop in Symbolic Data Analysis (SDA 2014), Academia Sinica, Taipei, Taiwan, 14-16 June 2014. (Invited speaker)

9.      M.S. Yang, W.L. Hung and S.J. Chang-Chien, 2014, A robust clustering algorithm for directional data, Proceedings of International Conference Data Mining, Civil and Mechanical Engineering (ICDMCME’2014), Bali, Indonesia, 4-5 February 2014, pp. 54-59.

10.      M.S. Yang, 2013, Cluster-Wise Regression Analysis Using Fuzzy Set Theory, 2013 NIMS Hot Topics Workshops on Prediction Using Fuzzy Theory, NIMS, Daejeon, Korea, 12-14 August 2013. (Invited speaker)

11.      M.S. Yang and Y.S. Pan, 2013, Sample-weighted fuzzy clustering with regularizations, Proceedings of 2013 International Conference on Machine Vision, Image Processing, and Pattern Analysis (ICMVIPPA-2013), Stockholm, Sweden, 15-16 July 2013, pp. 1276-1279.

12.      M.S. Yang, 2013, On robust expectation & maximization clustering algorithm, Proceedings of the 12th International Conference on Applied Computer and Applied Computational Science (ACACOS’13), Kuala Lumpur, Malaysia, 2-4 April 2013, p. 14. (Invited plenary speaker)

13.      M.S. Yang and W.Q. Lin, 2012, A weighted correlation coefficient control chart pattern recognition system, IEEE Proceedings of 2012International Conference on Mechanic Automation and Control Engineering (MACE-2012), Baotou, Inner Mongolia, China, 27-29 July 2012, pp. 2082-2085.

14.      M.S. Yang, H.C. Kuo and W.L. Hung, 2012, A robust clustering algorithm for interval data, Proceedings of WCCI 2012 IEEE World Congress on Computational Intelligence, FUZZ-IEEE 2012, Brisbane, Australia, 10-15 June 2012, pp. 2146-2152.

15.      M.S. Yang, 2012, On robust possibilistic c-means clustering algorithm, Proceedings of the 11th WSEAS International Conference on Applied Computers and Applied Computational Science, Rovaniemi, Finland, 18-20 April 2012, p. 12. (Invited plenary speaker)

16.      C.N. Wang and M.S. Yang, 2012, T-transitive interval-valued fuzzy relations for clustering, IEEE Proceedings of 2012 International Conference on Computer Distributed Control and Intelligent Enviromental Monitoring, Zhangjiajie, China, 5-6 March 2012, pp. 822-826.

17.      W.L. Hung, M.S. Yang and C.M. Hwang, 2011, Exponential-Distance Weighted K-Means Algorithm with Spatial Constraints for Color Image Segmentation, IEEE Proceedings of 2011 International Conference on Multimedia and Signal Processing, Guilin, China, 13-15 May 2011, pp. 131-135.

18.      C.M. Hwang, M.S. Yang and W.L. Hung, 2011, New similarity and inclusion measures between type-2 fuzzy sets. Proceedings of IEEESymposium Series on Computational Intelligence, IEEE-SSCI2011 on T2FUZZ-2011, Paris, France, 11-15 April 2011, pp. 82-87.

19.      C.M. Hwang, W.L. Hung and M.S. Yang, 2011, Adaptive weighted k-means algorithm for color image segmentation, Proceedings of the 2011 3rd International Conference on Computer Modeling and Simulation, ICCMS 2011, January 7-9, 2011, Mumbai, India, Vol. 1, pp. 282-286.

20.      M.S. Yang and W.Q. Lin, 2012, A weighted correlation coefficient control chart pattern recognition system, IEEE Proceedings of 2012International Conference on Mechanic Automation and Control Engineering (MACE-2012), Baotou, Inner Mongolia, China, 27-29 July 2012, pp. 2082-2085.

21.      M.S. Yang, H.C. Kuo and W.L. Hung, 2012, A robust clustering algorithm for interval data, Proceedings of WCCI 2012 IEEE World Congress on Computational Intelligence, FUZZ-IEEE 2012, Brisbane, Australia, 10-15 June 2012, pp. 2146-2152.

22.      M.S. Yang, 2012, On robust possibilistic c-means clustering algorithm, Proceedings of the 11th WSEAS International Conference on Applied Computers and Applied Computational Science, Rovaniemi, Finland, 18-20 April 2012, p. 12. (Invited plenary speaker)

23.      C.N. Wang and M.S. Yang, 2012, T-transitive interval-valued fuzzy relations for clustering, IEEE Proceedings of 2012 International Conference on Computer Distributed Control and Intelligent Enviromental Monitoring, Zhangjiajie, China, 5-6 March 2012, pp. 822-826.

24.      W.L. Hung, M.S. Yang and C.M. Hwang, 2011, Exponential-Distance Weighted K-Means Algorithm with Spatial Constraints for Color Image Segmentation, IEEE Proceedings of 2011 International Conference on Multimedia and Signal Processing, Guilin, China, 13-15 May 2011, pp. 131-135.

25.      C.M. Hwang, M.S. Yang and W.L. Hung, 2011, New similarity and inclusion measures between type-2 fuzzy sets. Proceedings of IEEESymposium Series on Computational Intelligence, IEEE-SSCI2011 on T2FUZZ-2011, Paris, France, 11-15 April 2011, pp. 82-87.

26.      C.M. Hwang, W.L. Hung and M.S. Yang, 2011, Adaptive weighted k-means algorithm for color image segmentation, Proceedings of the 2011 3rd International Conference on Computer Modeling and Simulation, ICCMS 2011, January 7-9, 2011, Mumbai, India, Vol. 1, pp. 282-286.

27.      S.J. Chang-Chien, M.S. Yang and W.L. Hung, 2010, Mean shift-based clustering for directional data, Proceedings of Third International Workshop on Advanced Computational Intelligence, Suzhou, China, 25-27 August 2010, pp. 367-372.

28.      D.H. Chen, W.L. Hung and M.S. Yang, 2010, A batch version of the SOM for symbolic data, Proceedings of the 2010 Sixth International Conference on Natural Computation, ICNC 2010, Yantai, China, 10-12 August 2010, pp. 1-5.

29.      W.L. Hung and M.S. Yang, 2010, A similarity-based clustering algorithm for fuzzy data, Proceedings of the 2010 IEEE World Congress on Computational Intelligence, FUZZ-IEEE 2010, Barcelona, Spain, 18-23 July 2010, pp. 443-448.

30.      M.S. Yang, 2010, On robust fuzzy clustering and validity indexes, Recent Advances in Neural Networks, Fuzzy Systems & Evolutionary Computing (In: Proceedings of the 11th WSEAS International Conference on Fuzzy Systems), Iasi, Romania, 13-15 June 2010, p. 16. (Invited plenary speaker)

31.      M.S. Yang, J.H Yang and C.Y. Lai, 2010, An integrated control chart pattern recognition system using correlation coefficient method and RBF neural networks, Proceedings of the Sixth IASTED International Conference on Advances in Computer Science and Engineering (ACSE 2010), Sharm El Sheikh, Egypt, 15-17 March 2010, pp. 67-73.

32.      D.H. Chen, M.S. Yang and W.L. Hung, 2009, A modified SOM learning algorithm for mixed types of symbolic and fuzzy data, Proceedings of 2009 WSEAS International Conference on Mathematical Methods and Applied Computing, Athens, Greece, 28-30 September 2009, pp. 150-155.

33.      M.S. Yang and Chih-Ying Lin, 2009, Block fuzzy k-modes clustering algorithm, Proceedings of the 2009 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2009, Jeju, Korea, 20-24 August 2009, pp. 384-389.

34.      W.L. Hung, M.S. Yang and D.H. Chen, 2009, Segmentation in MRI of ophthalmology using a robust-type clustering algorithms, Proceedings of the 2009 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2009, Jeju, Korea, 20-24 August 2009, pp. 427-430.

35.      M.S. Yang and J.H. Yang, 2009, On parameter estimation of control chart  patterns using RBF neural network, Proceedings of the 4th IEEE Conference on Industrial Electronics and Applications, Xi’an, China, 25-27 May 2009, pp. 1498-1502.

36.      K.L. Wu, M.S. Yang and J.N. Hsieh, 2009, Alternative fuzzy switching regression, Proceedings of International Multi-Conference of Engineers and Computer Scientists 2009, IMECS 2009, Hong Kong, 18-20 March 2009, pp. 761-765.

37.      M.S. Yang and C.Y. Lai, 2008, Entropy-type classification maximum likelihood Method, Proceedings of the Second IASTED Africa Conference on Modelling and Simulation, AfricaMS 2008, Gaborone, Botswana, 8-10 September 2008, pp. 205-209.

38.      W.L. Hung, M.S. Yang and D.H. Chen, 2008, Variation approaches to feature-weight selection and application to fuzzy clustering, Proceedings of the 2008 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2008, Hong Kong, 1-6 June 2008, pp. 276-280.

39.      M.S. Yang, K.C.R. Lin, H.C. Liu and J.F. Lirng, 2008, A fuzzy-soft competitive learning algorithm for ophthalmological MRI segmentation, Proceedings of the 2008 WSEAS International Conference on Applied Computing, Istanbul, Turkey, 27-30 May 2008, pp. 228-232.

40.      M.S. Yang and J.H. Yang, 2007, Control chart pattern recognition using semi-supervised learning, Proceedings of the 7th WSEAS International Conference on Applied Computer Science, Venice, Italy, 21-23 November 2007, pp. 272-276.

41.      D.C. Lin and M.S. Yang, 2007, A similarity measure between type-2 fuzzy sets with its application to clustering, Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD-2007, Haikou, Hainan, China, 24-27 August 2007, vol. 1, pp. 726-731.

42.      W.L. Hung, M.S. Yang and D.H. Chen 2007, Color image segmentation using Cauchy-type fuzzy c-means algorithm, Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD-2007, Haikou, Hainan, China, 24-27 August 2007, vol. 2, pp. 230-234.

43.      M.S. Yang, K.L. Wu and J.N. Hsieh, 2007, Mountain c-regressions in comparing fuzzy c-regressions, Proceedings of the 2007 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2007, London, UK, 23-26 July 2007, pp. 205-210.

44.      M.S. Yang, W.L. Hung and C.H. Chang, 2006, A penalized fuzzy clustering algorithm, Proceedings of the 6th WSEAS International Conference on Applied Computer Science, Tenerife, Canary Islands, Spain, 16-18 December 2006, pp. 13-18.

45.      M.S. Yang, W.L. Hung and T.I. Chung, 2006, Alternative fuzzy c-means clustering algorithms with L1-norm and covariance matrix, Proceedings of the 8th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2006, Antwerp, Belgium, 18-21 September 2006, pp. 654-665.

46.      M.S. Yang and H.S. Tsai, 2005, An alternative fuzzy compactness and separation clustering algorithm, Proceedings of the 7th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2005, Antwerp, Belgium, pp. 146-153.

47.      M.S. Yang and K.L. Wu, 2004, An unsupervised alternating clustering method Proceedings of the 2nd IASTED International Conference on Neural Networks and Computational Intelligence, Grindelwald, Switzerland, pp. 269-273.

48.      M.S. Yang, K.L. Wu and J. Yu, 2003, A novel fuzzy clustering algorithm, Proceedings of the 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2003, Kobe, Japan, pp. 647-652, July.

49.      M.S. Yang and K.L. Wu, 2002, A possibilistic type of alternative fuzzy c-means, Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2002, Honolulu, USA, pp. 1456-1459, May.

50.      Y.Y. Hong, C.N. Chang-Chien, K.L. Wu and M.S. Yang, 2002, Determination of congestion zones in deregulated electricity markets using fuzzy clustering, Proceeding of the 14th power Systems Computation Conference, PSCC02, Sevilla, Spain, Session 27, paper 2, pp. 1-7.

51.      M.S. Yang and H.M. Chen, 2001, Logistic regression analysis with fuzzy clustering algorithm,中華民國第九屆模糊理論與應用研討會, pp.74~79.

52.      M.S. Yang and K.L. Wu, 2001, A new validity index for fuzzy clustering, Proceedings of the 10th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2001, Melbourne, Australia, pp. 89-92.

53.      C.C. Hung, M.S. Yang, S.Y. Shin and T.L. Coleman, 2000, Evolution, fuzzy logic, and neural networks in unsupervised training algorithms, Proceedings of the International Association for Computer and Information Science 1st International Conference, SNPD’00, pp. 464-468.

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其他

1. 楊敏生、劉曼君 (1996), “可能性理論簡介” , 數學傳播 . 
2. 楊敏生 (1994), “模糊理論簡介” , 數學傳播 .

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著作

模糊聚類及其應用     

作者：楊敏生、楊鎮槐   

出版社：藍海文化

購書網址：三民網路書局