Bacterial Colony Algorithms for Association Rule Mining in Static and Stream Data

dc.contributor.authorCunha D.S.D.
dc.contributor.authorXavier R.S.
dc.contributor.authorFerrari D.G.
dc.contributor.authorVilasboas F.G.
dc.contributor.authorDe Castro L.N.
dc.date.accessioned2024-03-12T23:59:03Z
dc.date.available2024-03-12T23:59:03Z
dc.date.issued2018
dc.description.abstract© 2018 Danilo S. da Cunha et al.Bacterial colonies perform a cooperative and distributed exploration of the environmental resources by using their quorum-sensing mechanisms. This paper describes how bacterial colony networks and their skills to explore resources can be used as tools for mining association rules in static and stream data. A new algorithm is designed to maintain diverse solutions to the problems at hand, and its performance is compared to that of other well-known bacteria, genetic, and immune-inspired algorithms: Bacterial Foraging Optimization (BFO), a Genetic Algorithm (GA), and the Clonal Selection Algorithm (CLONALG). Taking into account the superior performance of our approach in static data, we applied the algorithms to dynamic environments by converting static into flow data via a stream data model named sliding-window. We also provide some notes on the running time of the proposed algorithm using different hardware and software architectures.
dc.description.volume2018
dc.identifier.doi10.1155/2018/4676258
dc.identifier.issn1563-5147
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/35597
dc.relation.ispartofMathematical Problems in Engineering
dc.rightsAcesso Aberto
dc.titleBacterial Colony Algorithms for Association Rule Mining in Static and Stream Data
dc.typeArtigo
local.scopus.citations4
local.scopus.eid2-s2.0-85057357618
local.scopus.subjectBacterial foraging optimizations (BFO)
local.scopus.subjectClonal selection algorithms
local.scopus.subjectDistributed exploration
local.scopus.subjectDynamic environments
local.scopus.subjectEnvironmental resources
local.scopus.subjectHardware and software architectures
local.scopus.subjectImmune-inspired algorithms
local.scopus.subjectMining associations
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85057357618&origin=inward
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