Evolutionary Computation in Intelligent Network
Management
A. Abraham. Evolutionary Computing in Data Mining, volume 163 of Studies in Fuzziness and Soft Computing, chapter 9, Springer, (2004)
Abstract
Data mining is an iterative and interactive process
concerned with discovering patterns, associations and
periodicity in real world data. This chapter presents
two real world applications where evolutionary
computation has been used to solve network management
problems. First, we investigate the suitability of
linear genetic programming (LGP) technique to model
fast and efficient intrusion detection systems, while
comparing its performance with artificial neural
networks and classification and regression trees.
Second, we use evolutionary algorithms for a Web
usage-mining problem. Web usage mining attempts to
discover useful knowledge from the secondary data
obtained from the interactions of the users with the
Web. Evolutionary algorithm is used to optimise the
concurrent architecture of a fuzzy clustering algorithm
(to discover data clusters) and a fuzzy inference
system to analyse the trends. Empirical results clearly
shows that evolutionary algorithm could play a major
rule for the problems considered and hence an important
data mining tool.
%0 Book Section
%1 abraham:2004:ECDM
%A Abraham, Ajith
%B Evolutionary Computing in Data Mining
%D 2004
%E Ghosh, Ashish
%E Jain, Lakhmi C.
%I Springer
%K (ring 32-bit ANN, FPU GP, Genetic LGP, Linear Programming, RIPPER, SVM, algorithms, clustering, code computer decision demes detection, fuzzy genetic i-miner inference, intrusion machine programming, security, state steady topology), trees, www,
%P 189--210
%T Evolutionary Computation in Intelligent Network
Management
%U http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980376-0,00.html
%V 163
%X Data mining is an iterative and interactive process
concerned with discovering patterns, associations and
periodicity in real world data. This chapter presents
two real world applications where evolutionary
computation has been used to solve network management
problems. First, we investigate the suitability of
linear genetic programming (LGP) technique to model
fast and efficient intrusion detection systems, while
comparing its performance with artificial neural
networks and classification and regression trees.
Second, we use evolutionary algorithms for a Web
usage-mining problem. Web usage mining attempts to
discover useful knowledge from the secondary data
obtained from the interactions of the users with the
Web. Evolutionary algorithm is used to optimise the
concurrent architecture of a fuzzy clustering algorithm
(to discover data clusters) and a fuzzy inference
system to analyse the trends. Empirical results clearly
shows that evolutionary algorithm could play a major
rule for the problems considered and hence an important
data mining tool.
%& 9
%@ 3-540-22370-3
@incollection{abraham:2004:ECDM,
abstract = {Data mining is an iterative and interactive process
concerned with discovering patterns, associations and
periodicity in real world data. This chapter presents
two real world applications where evolutionary
computation has been used to solve network management
problems. First, we investigate the suitability of
linear genetic programming (LGP) technique to model
fast and efficient intrusion detection systems, while
comparing its performance with artificial neural
networks and classification and regression trees.
Second, we use evolutionary algorithms for a Web
usage-mining problem. Web usage mining attempts to
discover useful knowledge from the secondary data
obtained from the interactions of the users with the
Web. Evolutionary algorithm is used to optimise the
concurrent architecture of a fuzzy clustering algorithm
(to discover data clusters) and a fuzzy inference
system to analyse the trends. Empirical results clearly
shows that evolutionary algorithm could play a major
rule for the problems considered and hence an important
data mining tool.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Abraham, Ajith},
biburl = {https://www.bibsonomy.org/bibtex/21b918ed3fc8dfa9f4b9bcaed3ead1929/brazovayeye},
booktitle = {Evolutionary Computing in Data Mining},
chapter = 9,
editor = {Ghosh, Ashish and Jain, Lakhmi C.},
interhash = {ca424a947aba0eaf160739fc2d291d64},
intrahash = {1b918ed3fc8dfa9f4b9bcaed3ead1929},
isbn = {3-540-22370-3},
keywords = {(ring 32-bit ANN, FPU GP, Genetic LGP, Linear Programming, RIPPER, SVM, algorithms, clustering, code computer decision demes detection, fuzzy genetic i-miner inference, intrusion machine programming, security, state steady topology), trees, www,},
pages = {189--210},
publisher = {Springer},
series = {Studies in Fuzziness and Soft Computing},
size = {22 pages},
timestamp = {2008-06-19T17:35:10.000+0200},
title = {Evolutionary Computation in Intelligent Network
Management},
url = {http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980376-0,00.html},
volume = 163,
year = 2004
}