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Algorithms for Real-Time Clustering and Generation of Rules from Data

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Published

Standard

Algorithms for Real-Time Clustering and Generation of Rules from Data. / Filev, Dimitar; Angelov, Plamen.
Advances in Fuzzy Clustering and Its Applications. ed. / J V de Oliveira; W Pedrycz. Chichester: John Willey and Sons, 2007. p. 353-370.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Harvard

Filev, D & Angelov, P 2007, Algorithms for Real-Time Clustering and Generation of Rules from Data. in JV de Oliveira & W Pedrycz (eds), Advances in Fuzzy Clustering and Its Applications. John Willey and Sons, Chichester, pp. 353-370. <http://bullebooks.files.wordpress.com/2007/10/advances-in-fuzzy-clustering-and-its-applications-frontmatter.pdf>

APA

Filev, D., & Angelov, P. (2007). Algorithms for Real-Time Clustering and Generation of Rules from Data. In J. V. de Oliveira, & W. Pedrycz (Eds.), Advances in Fuzzy Clustering and Its Applications (pp. 353-370). John Willey and Sons. http://bullebooks.files.wordpress.com/2007/10/advances-in-fuzzy-clustering-and-its-applications-frontmatter.pdf

Vancouver

Filev D, Angelov P. Algorithms for Real-Time Clustering and Generation of Rules from Data. In de Oliveira JV, Pedrycz W, editors, Advances in Fuzzy Clustering and Its Applications. Chichester: John Willey and Sons. 2007. p. 353-370

Author

Filev, Dimitar ; Angelov, Plamen. / Algorithms for Real-Time Clustering and Generation of Rules from Data. Advances in Fuzzy Clustering and Its Applications. editor / J V de Oliveira ; W Pedrycz. Chichester : John Willey and Sons, 2007. pp. 353-370

Bibtex

@inbook{eec2d0939b744e6ab49c775f1041d338,
title = "Algorithms for Real-Time Clustering and Generation of Rules from Data",
abstract = "The problem of real-time clustering has gained considerable attention in recent years in conjunction with the advances in the areas of multiple model representation of complex systems, summarization of information, and novelty detection for diagnostics and prognostics. This chapter deals with two main approaches for real-time clustering – the first algorithm is density based and is derived from the Mountain/Subtractive clustering method while the second one is distance based and has its roots in the k-nearest neighbors (k-NN) and self-organizing maps (SOM) clustering methods. Applications of these algorithms for extraction of rules from data, for control of complex systems with multiple operating modes, fault detection, and prognostics are presented in the chapter. (c) John Willey and Sons",
keywords = "real-time, on-line clustering, fuzzy rules, mountain, subtractive clustering, statistical process control, DCS-publications-id, incoll-64, DCS-publications-credits, dsp, DCS-publications-personnel-id, 82",
author = "Dimitar Filev and Plamen Angelov",
year = "2007",
language = "English",
isbn = "978-0-470-02760-8",
pages = "353--370",
editor = "{de Oliveira}, {J V} and W Pedrycz",
booktitle = "Advances in Fuzzy Clustering and Its Applications",
publisher = "John Willey and Sons",

}

RIS

TY - CHAP

T1 - Algorithms for Real-Time Clustering and Generation of Rules from Data

AU - Filev, Dimitar

AU - Angelov, Plamen

PY - 2007

Y1 - 2007

N2 - The problem of real-time clustering has gained considerable attention in recent years in conjunction with the advances in the areas of multiple model representation of complex systems, summarization of information, and novelty detection for diagnostics and prognostics. This chapter deals with two main approaches for real-time clustering – the first algorithm is density based and is derived from the Mountain/Subtractive clustering method while the second one is distance based and has its roots in the k-nearest neighbors (k-NN) and self-organizing maps (SOM) clustering methods. Applications of these algorithms for extraction of rules from data, for control of complex systems with multiple operating modes, fault detection, and prognostics are presented in the chapter. (c) John Willey and Sons

AB - The problem of real-time clustering has gained considerable attention in recent years in conjunction with the advances in the areas of multiple model representation of complex systems, summarization of information, and novelty detection for diagnostics and prognostics. This chapter deals with two main approaches for real-time clustering – the first algorithm is density based and is derived from the Mountain/Subtractive clustering method while the second one is distance based and has its roots in the k-nearest neighbors (k-NN) and self-organizing maps (SOM) clustering methods. Applications of these algorithms for extraction of rules from data, for control of complex systems with multiple operating modes, fault detection, and prognostics are presented in the chapter. (c) John Willey and Sons

KW - real-time

KW - on-line clustering

KW - fuzzy rules

KW - mountain

KW - subtractive clustering

KW - statistical process control

KW - DCS-publications-id

KW - incoll-64

KW - DCS-publications-credits

KW - dsp

KW - DCS-publications-personnel-id

KW - 82

M3 - Chapter

SN - 978-0-470-02760-8

SP - 353

EP - 370

BT - Advances in Fuzzy Clustering and Its Applications

A2 - de Oliveira, J V

A2 - Pedrycz, W

PB - John Willey and Sons

CY - Chichester

ER -