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Pattern Recognition with Fuzzy Objective Function Algorithms
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Citations
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Journal ArticleDOI
FUZZY QMODEL—A new approach for linear unmixing
TL;DR: FUZZY QMODEL utilizes the fuzzy c-means algorithm of Bezdek to provide an alternative initial mixing polyhedron and so can produce suitable solutions in the presence of noisy or “messy” data points.
Proceedings ArticleDOI
A Group Formation Tool in an E-Learning Context
TL;DR: A Web-based group formation tool that supports the instructor to automatically create both homogeneous and heterogeneous groups based on up to three criteria and the learner to negotiate the grouping and the learners to be informed for the groups formed.
Journal ArticleDOI
On the use of the weighted fuzzy c-means in fuzzy modeling
TL;DR: A fuzzy clustering-based algorithm for fuzzy modeling that incorporates unsupervised learning with an iterative process into a framework, which is based on the use of the weighted fuzzy c-means, which proves to be very accurate as well as compact in size.
Book ChapterDOI
Cluster Analysis: Basic Concepts and Methods
TL;DR: This chapter presents the basic concepts and methods of cluster analysis, the process of grouping a set of data objects into multiple groups or clusters so that objects within a cluster have high similarity, but are very dissimilar to objects in other clusters.
Journal ArticleDOI
Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction
TL;DR: A new hybrid soft computing for bankruptcy prediction was proposed in which novel fitness function designs are presented for the GA based financial ratio selection and a fuzzy clustering algorithm was used for the classifier design.
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.