Abstract
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.
Highlights
Bacterial colonies can be seen as complex adaptive systems that perform distributed information processing to solve complex problems, such as food acquisition, swarming mobility, and biofilm formation, among others
This paper presents an algorithm inspired by the exploratory behaviour of environmental resources by a colony of bacteria, named Bacterial Colony Association Rule Optimization (BaCARO)-II, extended from [5, 6], for mining association rules of items in transactional databases and introduces the necessary modifications so that it can be applied to data streams
Algorithm (BCA) [34] and bacterial foraging behaviours have been used as inspiration for the design of other algorithms, such as the Bacterial Foraging Optimization (BFO) Algorithm [8], Bacterial Colony Optimization (BCO) [35], and Bacterial Colony Association Rule Optimization (BaCARO) [5, 6]
Summary
Bacterial colonies can be seen as complex adaptive systems that perform distributed information processing to solve complex problems, such as food acquisition, swarming mobility, and biofilm formation, among others. They use a collaborative system of chemical signals to explore the resources of a given environment and coordinate their social and behavioural tasks [1]. This paper presents an algorithm inspired by the exploratory behaviour of environmental resources by a colony of bacteria, named BaCARO-II, extended from [5, 6], for mining association rules of items in transactional databases and introduces the necessary modifications so that it can be applied to data streams. GA: Genetic Algorithm sBaCARO-II: Stream Bacterial Colony Association Rule Optimization-II sBFO: Stream Bacterial Foraging Optimization Algorithm sCLONALG: Stream Clonal Selection Algorithm sGA: Stream Genetic Algorithm
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