An improving fuzzy ant clustering using artificial immune recognition system

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dc.contributor.authorWerasak Kurutach
dc.contributor.authorSurat Srinoy
dc.contributor.authorWitcha Chimphlee
dc.contributor.authorSiriporn Chimphlee
dc.date.accessioned2025-03-10T07:38:08Z
dc.date.available2025-03-10T07:38:08Z
dc.date.issued2006
dc.description.abstractWe present a swarm intelligence approach to data clustering. Ant based clustering is used to initially create raw clusters and then these clusters are refined using Artificial Immune Recognition System (AIRS). AIRS that has been developed as an immune-inspired supervise learning algorithm. Certain unnecessary complications of the original algorithm are discussed and means of overcoming these complexities are proposed. We propose artificial immune recognition systems (AIRS) in the second stage for refinement mean of overcoming these complexities are proposed. Our approach allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) dataset.
dc.identifier.citationLecture Notes in Engineering and Computer Science
dc.identifier.isbn978-988986713-3
dc.identifier.issn20780958
dc.identifier.scopus2-s2.0-84888272423
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5069
dc.languageEnglish
dc.rights.holderScopus
dc.subjectAnomaly Detection
dc.subjectAnt Based Clustering
dc.subjectArtificial Immune Recognition System
dc.subjectClustering
dc.subjectSwarm Intelligence
dc.titleAn improving fuzzy ant clustering using artificial immune recognition system
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84888272423&partnerID=40&md5=d7b748532fe9a0f99bd08066c3ad988e
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oaire.citation.startPage18
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