Bacterial Colony Algorithms for Association Rule Mining in Static and Stream Data
Tipo
Artigo
Data de publicação
2018
Periódico
Mathematical Problems in Engineering
Citações (Scopus)
4
Autores
Cunha D.S.D.
Xavier R.S.
Ferrari D.G.
Vilasboas F.G.
De Castro L.N.
Xavier R.S.
Ferrari D.G.
Vilasboas F.G.
De Castro L.N.
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Resumo
© 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.
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Assuntos Scopus
Bacterial foraging optimizations (BFO) , Clonal selection algorithms , Distributed exploration , Dynamic environments , Environmental resources , Hardware and software architectures , Immune-inspired algorithms , Mining associations