An efficient Meta-cognitive Fuzzy C-Means clustering approach

Applied Soft Computing - Tập 85 - Trang 105838 - 2019
S.V. Aruna Kumar1, B.S. Harish2, B.S. Mahanand2, N. Sundararajan2
1Department of Computer Science, University of Beira Interior, Covilha, Portugal
2Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, Karnataka, India

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