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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Book ChapterDOI
Swarm Intelligence Algorithms for Data Clustering
TL;DR: A new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization is described, which may come with a variety of attributes or features.
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Model-free functional MRI analysis using Kohonen clustering neural network and fuzzy C-means
TL;DR: This method can efficiently and stably identify the actual functional response with typical signal change to noise ratio, from a small activation area occupying only 0.2% of head size, with phase delay, and from other noise sources such as head motion.
Journal ArticleDOI
A clustering algorithm for fuzzy model identification
TL;DR: The fuzzy model proposed by Takagi and Sugeno can represent highly nonlinear systems and is widely used for the representation of fuzzy rules and an identification scheme for rule's premise and consequence parameters is deduced from the clustering algorithm in succession.
Journal ArticleDOI
Uncovering and Treating Unobserved Heterogeneity with FIMIX-PLS: Which Model Selection Criterion Provides an Appropriate Number of Segments?
TL;DR: In this paper, the performance and robustness of several model selection criteria in determining an adequate number of segments has been investigated scientifically in the context of finite mixture partial least squares (FIMIX-PLS) in response-based segmentation in variance-based structural equation modeling.
Journal ArticleDOI
Alternating cluster estimation: a new tool for clustering and function approximation
TL;DR: Out of a large variety of possible instances of non-AO models, an algorithm with a dynamically changing prototype function that extracts representative data and a computationally efficient algorithm with hyperconic membership functions that allows easy extraction of membership functions are presented.
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.