Evolutionary and Immune Algorithms Applied to Association Rule Mining in Static and Stream Data

dc.contributor.authorDa Cunha D.S.
dc.contributor.authorDe Castro L.N.
dc.date.accessioned2024-03-12T23:56:50Z
dc.date.available2024-03-12T23:56:50Z
dc.date.issued2018
dc.description.abstract© 2018 IEEE.Data generation has grown rapidly over the recent years. Different types of products and services are offered daily on the Internet. Finding out elegant, flexible and robust strategies to deal with this amount of data in a static way is one goal of data mining, whilst the data stream mining works in dynamic environments. The searching of co-occurrence of items in data is a task of a data miming branch named association rule mining. The present paper investigates the use of evolutionary algorithms as well as artificial immune systems to extract association rules within item sets in both, static and dynamic, environments. We perform a number of experiments over datasets from the association rule mining literature, and compare their performances. A discussion in terms of computational time and measures of interest is made to conclude the proposed study.
dc.identifier.doi10.1109/CEC.2018.8477978
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/35472
dc.relation.ispartof2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
dc.rightsAcesso Restrito
dc.subject.otherlanguageArtificial Immune Systems
dc.subject.otherlanguageData Mining
dc.subject.otherlanguageData Stream
dc.subject.otherlanguageEvolutionary Algorithms
dc.titleEvolutionary and Immune Algorithms Applied to Association Rule Mining in Static and Stream Data
dc.typeArtigo de evento
local.scopus.citations5
local.scopus.eid2-s2.0-85056263018
local.scopus.subjectArtificial Immune System
local.scopus.subjectComputational time
local.scopus.subjectData generation
local.scopus.subjectData stream
local.scopus.subjectData stream mining
local.scopus.subjectDynamic environments
local.scopus.subjectImmune algorithms
local.scopus.subjectProducts and services
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85056263018&origin=inward
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