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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Journal ArticleDOI
Multiple Kernel Fuzzy Clustering
TL;DR: A multiple kernel fuzzy c-means (MKFC) algorithm that is more immune to ineffective kernels and irrelevant features and automatically adjusting the kernel weights, which makes the choice of kernels less crucial.
Journal ArticleDOI
An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation
Alan Wee-Chung Liew,Hong Yan +1 more
TL;DR: An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images that takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels.
Book ChapterDOI
Data mining for web personalization
TL;DR: An overview of Web personalization process viewed as an application of data mining requiring support for all the phases of a typical data mining cycle, including data collection and pre-processing, pattern discovery and evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web.
Proceedings ArticleDOI
A mixed c-means clustering model
TL;DR: F fuzzy-possibilistic c-means is proposed, and it is shown that FPCM solves the noise sensitivity defect of FCM, and also overcomes the coincident clusters problem of PCM.
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Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging.
TL;DR: The complexity of lesions segmentation is described, the automatic MS lesion segmentation methods found, and the validation methods applied are reviewed, to evaluate the state of the art in automated multiple sclerosis lesion segmentsation.
References
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Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.