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Pattern Recognition with Fuzzy Objective Function Algorithms
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Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land
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Construction of fuzzy models through clustering techniques
TL;DR: In comparison to the algorithms existing in the literature and producing function-like models, the proposed fuzzy models designed with the aid of fuzzy clustering is of a relational character allowing for multidirectional accessibility.
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Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm
TL;DR: A scheme for clustering complex and linearly non-separable datasets, without any prior knowledge of the number of naturally occurring groups in the data is introduced, based on a modified version of classical Particle Swarm Optimization algorithm, known as the Multi-Elitist PSO (MEPSO) model, which employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance.
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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.