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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Unsupervised learning of prototypes and attribute weights
H. Frigui,Olfa Nasraoui +1 more
TL;DR: New algorithms that perform clustering and feature weighting simultaneously and in an unsupervised manner are introduced and can be used in the subsequent steps of a learning system to improve its learning behavior.
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Overview and literature survey of fuzzy set theory in power systems
TL;DR: A survey of publications on applications of fuzzy set theory to power systems and the basic procedures for fuzzy set based methods to solve specific power systems problems is presented.
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Fuzzy clustering of time series data using dynamic time warping distance
TL;DR: Three alternatives for fuzzy clustering of time series using DTW distance are proposed, including a DTW-based averaging technique proposed in the literature, which has been applied to the Fuzzy C-Means clustering.
Journal ArticleDOI
New neutrosophic approach to image segmentation
Yanhui Guo,Heng-Da Cheng +1 more
TL;DR: This work applies neutrosophic set, a formal framework that has been recently proposed, for image segmentation, and demonstrates that the proposed approach can segment the images automatically and effectively.
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Color image segmentation using histogram thresholding - Fuzzy C-means hybrid approach
TL;DR: Experimental results have demonstrated that the low complexity of the proposed HTFCM approach could obtain better cluster quality and segmentation results than other segmentation approaches that employing ant colony algorithm.
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.