Journal ArticleDOI
Numerical methods for fuzzy clustering
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TLDR
In this paper, the authors considered the problem of decomposition of the probability density function of the original set into the weighted sum of the component fuzzy set densities, which is done by optimization of some functional defined over all possible fuzzy classifications.About:
This article is published in Information Sciences.The article was published on 1970-07-01. It has received 561 citations till now. The article focuses on the topics: Fuzzy clustering & Fuzzy set operations.read more
Citations
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Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
TL;DR: A new graphical display is proposed for partitioning techniques, where each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation, and provides an evaluation of clustering validity.
Book
Connectionist Speech Recognition: A Hybrid Approach
Hervé Bourlard,Nelson Morgan +1 more
TL;DR: Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance.
Journal ArticleDOI
The role of fuzzy logic in the management of uncertainty in expert systems
TL;DR: F fuzzy logic is suggested, which is the logic underlying approximate or, equivalently, fuzzy reasoning, which leads to various basic syllogisms which may be used as rules of combination of evidence in expert systems.
Book
Fuzzy Modeling for Control
TL;DR: Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering point of view and focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements, and on the design of nonlinear controllers based on fuzzy models.
References
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Journal ArticleDOI
A new approach to clustering
TL;DR: A new method of representation of the reduced data, based on the idea of “fuzzy sets,” is proposed to avoid some of the problems of current clustering procedures and to provide better insight into the structure of the original data.
Journal ArticleDOI
A clustering technique for summarizing multivariate data.
Geoffrey H. Ball,David J. Hall +1 more
TL;DR: A practical computing method termed ISODATA, which finds the cluster structure of such data, is described and provides a fit to the data of a set of cluster centers that tends to minimize the sum of the squared distances of each data point from its closest cluster center.