%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 }