An efficient Meta-cognitive Fuzzy C-Means clustering approach
Tài liệu tham khảo
Jain, 1988
Duda, 2012
Xu, 2008
Jain, 1999, Data clustering: a review, ACM Comput. Surv. (CSUR), 31, 264, 10.1145/331499.331504
Xu, 2005, Survey of clustering algorithms, IEEE Trans. Neural Netw., 16, 645, 10.1109/TNN.2005.845141
Berkhin, 2006, A survey of clustering data mining techniques, 25
Filippone, 2008, A survey of kernel and spectral methods for clustering, Pattern Recognit., 41, 176, 10.1016/j.patcog.2007.05.018
Xu, 2015, A comprehensive survey of clustering algorithms, Ann. Data Sci., 2, 165, 10.1007/s40745-015-0040-1
Everitt, 2011, Hierarchical clustering, 71
Hartigan, 1979, Algorithm AS 136: A k-means clustering algorithm, J. R. Stat. Soc. C, 28, 100
J. MacQueen, et al. Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, No. 14, 1967, Oakland, CA, USA, pp. 281–297.
Tîrnăucă, 2018, Global optimality in k-means clustering, Inform. Sci., 439, 79, 10.1016/j.ins.2018.02.001
Ahmed, 2002, A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data, IEEE Trans. Med. Imaging, 21, 193, 10.1109/42.996338
S.V.A. Kumar, B.S. Harish, Segmenting mri brain images using novel robust spatial kernel fcm (rskfcm), in: Eighth International Conference on Image and Signal Processing, 2014, pp.38–44.
Kumar, 2015, Segmenting MRI brain images using evolutionary computation technique, 1
Liew, 2003, An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation, IEEE Trans. Med. Imaging, 22, 1063, 10.1109/TMI.2003.816956
Liu, 2008, Kernelized fuzzy attribute C-means clustering algorithm, Fuzzy Sets and Systems, 159, 2428, 10.1016/j.fss.2008.03.018
Lin, 2014, A novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm, IEEE Trans. Fuzzy Syst., 22, 1074, 10.1109/TFUZZ.2013.2280141
Iakovidis, 2008, Intuitionistic fuzzy clustering with applications in computer vision, 764
Chaira, 2011, A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images, Appl. Soft Comput., 11, 1711, 10.1016/j.asoc.2010.05.005
Chaudhuri, 2015, Intuitionistic fuzzy possibilistic c means clustering algorithms, Adv. Fuzzy Syst., 2015, 1, 10.1155/2015/238237
Kumar, 2016, A picture fuzzy clustering approach for brain tumor segmentation, 1
Kumar, 2017, A modified intuitionistic fuzzy clustering algorithm for medical image segmentation, J. Intell. Syst., 27, 593, 10.1515/jisys-2016-0241
Masud, 2018, I-nice: A new approach for identifying the number of clusters and initial cluster centres, Inform. Sci., 466, 129, 10.1016/j.ins.2018.07.034
Meng, 2018, A new distance with derivative information for functional k-means clustering algorithm, Inform. Sci., 463–464, 166, 10.1016/j.ins.2018.06.035
Nelson, 1990, The psychology of learning and motivation
Suresh, 2010, A sequential learning algorithm for self-adaptive resource allocation network classifier, Neurocomputing, 73, 3012, 10.1016/j.neucom.2010.07.003
Suresh, 2011, A sequential learning algorithm for complex-valued self-regulating resource allocation network-CSRAN, IEEE Trans. Neural Netw., 22, 1061, 10.1109/TNN.2011.2144618
Babu, 2012, Meta-cognitive neural network for classification problems in a sequential learning framework, Neurocomputing, 81, 86, 10.1016/j.neucom.2011.12.001
Savitha, 2012, Metacognitive learning in a fully complex-valued radial basis function neural network, Neural Comput., 24, 1297, 10.1162/NECO_a_00254
Y. Fukuyama, A new method of choosing the number of clusters for the fuzzy c-mean method, in: Proc. 5th Fuzzy Syst. Symp., 1989, 1989, pp. 247–250.
Xie, 1991, A validity measure for fuzzy clustering, IEEE Trans. Pattern Anal. Mach. Intell., 13, 841, 10.1109/34.85677
Dunn, 1973
Bezdek, 1984, FCM: The fuzzy c-means clustering algorithm, Comput. Geosci., 10, 191, 10.1016/0098-3004(84)90020-7
Wang, 2007, On fuzzy cluster validity indices, Fuzzy Sets and Systems, 158, 2095, 10.1016/j.fss.2007.03.004
Asuncion, 2007
Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7, 1
Dunn, 1961, Multiple comparisons among means, J. Amer. Statist. Assoc., 56, 52, 10.1080/01621459.1961.10482090
Zhang, 2004, A novel kernelized fuzzy c-means algorithm with application in medical image segmentation, Artif. Intell. Med., 32, 37, 10.1016/j.artmed.2004.01.012
Volkmar, 1989, An examination of social typologies in autism, J. Am. Acad. Child Adolesc. Psychiatry, 28, 82, 10.1097/00004583-198901000-00015
Eisenmajer, 1996, Comparison of clinical symptoms in autism and asperger’s disorder, J. Am. Acad. Child Adolesc. Psychiatry, 35, 1523, 10.1097/00004583-199611000-00022
Ashburner, 2000, Voxel-based morphometry—the methods, Neuroimage, 11, 805, 10.1006/nimg.2000.0582
Riddle, 2017, Brain structure in autism: a voxel-based morphometry analysis of the autism brain imaging database exchange (ABIDE), Brain Imaging Behav., 11, 541, 10.1007/s11682-016-9534-5
Pagnozzi, 2018, A systematic review of structural MRI biomarkers in autism spectrum disorder: A machine learning perspective, Int. J. Dev. Neurosci., 71, 68, 10.1016/j.ijdevneu.2018.08.010
Vigneshwaran, 2015, Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network, Expert Syst. Appl., 42, 8775, 10.1016/j.eswa.2015.07.031
Di Martino, 2014, The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism, Mol. Psychiatry, 19, 659, 10.1038/mp.2013.78
Ashburner, 2000, Voxel-based morphometry-the methods, NeuroImage, 11, 805, 10.1006/nimg.2000.0582
Ashburner, 2005, Unified segmentation, Neuroimage, 26, 839, 10.1016/j.neuroimage.2005.02.018