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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Daoqiang Zhang,Songcan Chen +1 more
TL;DR: A novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data is presented using a kernel-induced distance metric and a spatial penalty on the membership functions to compensate for the intensity inhomogeneities of MR image.
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Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation
TL;DR: An improved fuzzy C-means (FCM) algorithm for image segmentation is presented by introducing a tradeoff weighted fuzzy factor and a kernel metric and results show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.
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Identification of overlapping community structure in complex networks using fuzzy c-means clustering
TL;DR: A novel algorithm to identify overlapping communities in complex networks by the combination of a new modularity function based on generalizing NG's Q function, an approximation mapping of network nodes into Euclidean space and fuzzy c-means clustering is devised.
Journal ArticleDOI
Fuzzy C-means method for clustering microarray data
Doulaye Dembélé,Philippe Kastner +1 more
TL;DR: By setting threshold levels for the membership values of the FCM method, genes which are tigthly associated to a given cluster can be selected and this selection increases the overall biological significance of the genes within the cluster.
References
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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.