Home > Research > Publications & Outputs > Clustering as a tool for self-generation of int...

Research

Associated organisational units

Electronic data

View graph of relations

Clustering as a tool for self-generation of intelligent systems : a survey.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Published

Standard

Clustering as a tool for self-generation of intelligent systems : a survey. / Dutta Baruah, Rashmi; Angelov, Plamen.
2010. 34-41 Paper presented at Evolving Intelligent Systems, EIS'10, Leicester, UK.

Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review

Harvard

Dutta Baruah, R & Angelov, P 2010, 'Clustering as a tool for self-generation of intelligent systems : a survey.', Paper presented at Evolving Intelligent Systems, EIS'10, Leicester, UK, 29/03/10 - 1/04/10 pp. 34-41.

APA

Dutta Baruah, R., & Angelov, P. (2010). Clustering as a tool for self-generation of intelligent systems : a survey.. 34-41. Paper presented at Evolving Intelligent Systems, EIS'10, Leicester, UK.

Vancouver

Dutta Baruah R, Angelov P. Clustering as a tool for self-generation of intelligent systems : a survey.. 2010. Paper presented at Evolving Intelligent Systems, EIS'10, Leicester, UK.

Author

Dutta Baruah, Rashmi ; Angelov, Plamen. / Clustering as a tool for self-generation of intelligent systems : a survey. Paper presented at Evolving Intelligent Systems, EIS'10, Leicester, UK.9 p.

Bibtex

@conference{25d4e0ad0ce042b38e128651ee08f994,
title = "Clustering as a tool for self-generation of intelligent systems : a survey.",
abstract = "Fuzzy Rule Based (FRB) and Neuro-fuzzy systems are commonly used as a basis for intelligent systems due to their transparent and simple human interpretable structure. One of the crucial steps in designing FRB and neuro-fuzzy systems is to innovate the rule base. Data clustering is one of the approaches that have been applied extensively to automatically generate rules from input-output data. The goal of this paper is to critically review some of the most commonly used as well as recently developed clustering techniques, emphasizing their use in rule base generation. The paper explores the shift from offline clustering techniques to online and finally to evolving techniques that originated due to the current demand of adaptive systems.",
author = "{Dutta Baruah}, Rashmi and Plamen Angelov",
year = "2010",
month = apr,
day = "1",
language = "English",
pages = "34--41",
note = "Evolving Intelligent Systems, EIS'10 ; Conference date: 29-03-2010 Through 01-04-2010",

}

RIS

TY - CONF

T1 - Clustering as a tool for self-generation of intelligent systems : a survey.

AU - Dutta Baruah, Rashmi

AU - Angelov, Plamen

PY - 2010/4/1

Y1 - 2010/4/1

N2 - Fuzzy Rule Based (FRB) and Neuro-fuzzy systems are commonly used as a basis for intelligent systems due to their transparent and simple human interpretable structure. One of the crucial steps in designing FRB and neuro-fuzzy systems is to innovate the rule base. Data clustering is one of the approaches that have been applied extensively to automatically generate rules from input-output data. The goal of this paper is to critically review some of the most commonly used as well as recently developed clustering techniques, emphasizing their use in rule base generation. The paper explores the shift from offline clustering techniques to online and finally to evolving techniques that originated due to the current demand of adaptive systems.

AB - Fuzzy Rule Based (FRB) and Neuro-fuzzy systems are commonly used as a basis for intelligent systems due to their transparent and simple human interpretable structure. One of the crucial steps in designing FRB and neuro-fuzzy systems is to innovate the rule base. Data clustering is one of the approaches that have been applied extensively to automatically generate rules from input-output data. The goal of this paper is to critically review some of the most commonly used as well as recently developed clustering techniques, emphasizing their use in rule base generation. The paper explores the shift from offline clustering techniques to online and finally to evolving techniques that originated due to the current demand of adaptive systems.

M3 - Conference paper

SP - 34

EP - 41

T2 - Evolving Intelligent Systems, EIS'10

Y2 - 29 March 2010 through 1 April 2010

ER -