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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Journal ArticleDOI
A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Hichem Frigui,Raghu Krishnapuram +1 more
TL;DR: A clustering algorithm that combines the advantages of fuzzy clustering and robust statistical estimators is presented and an extension of the algorithm to deal with an unknown number of clusters is proposed.
Book
Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications
TL;DR: This book considers the principal constituents of soft computing, namely fuzzy logic, neurocomputing, genetic computing, and probabilistic reasoning, the relations between them, and their fusion in industrial applications, and how the combination can be used to achieve a high Machine Intelligence Quotient.
Journal ArticleDOI
A multistep unsupervised fuzzy clustering analysis of fMRI time series.
TL;DR: The performances of the proposed strategy are comparable to those of the classical approaches where extensive prior knowledge has to be added and its sensitivity and robustness is compared to the classical FCA or other techniques as a function of several parameters such as Contrast‐to‐Noise Ratio (CNR) and noise amplitude.
Journal ArticleDOI
Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty
TL;DR: It is concluded that the (multivariate) LBP texture model in combination with a hierarchical splitting segmentation framework is suitable for identifying objects and for quantifying their uncertainty.
Journal ArticleDOI
A cluster validity index for fuzzy clustering
TL;DR: A new cluster validity index is proposed for the validation of partitions of object data produced by the fuzzy c-means algorithm with superior effectiveness and reliability in comparison to other indices and the robustness of the proposed index in noisy environments.
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.