Swarm Intelligence and its Application in Abnormal Data Detection
Abstract
This study addresses swarm intelligence-based approaches in data quality detection. First, three typical
swarm intelligence models and their applications in abnormity detection are introduced, including Ant
Colony Optimization (ACO), Particle Swarm Optimization (PSO), Bee Colony Optimization (BCO).
Then, it presents three approaches based on ACO, PSO and BCO for detection of attribute outliers in
datasets. These approaches use different search strategies on the data items; however, they choose the
same fitness function (i.e. the O-measure) to evaluate the solutions, and they make use of swarms of the
fittest agents and random moving agents to obtain superior solutions by changing the searching paths or
positions of agents. Three algorithms are described and explained, which are efficient by heuristic
principles.
swarm intelligence models and their applications in abnormity detection are introduced, including Ant
Colony Optimization (ACO), Particle Swarm Optimization (PSO), Bee Colony Optimization (BCO).
Then, it presents three approaches based on ACO, PSO and BCO for detection of attribute outliers in
datasets. These approaches use different search strategies on the data items; however, they choose the
same fitness function (i.e. the O-measure) to evaluate the solutions, and they make use of swarms of the
fittest agents and random moving agents to obtain superior solutions by changing the searching paths or
positions of agents. Three algorithms are described and explained, which are efficient by heuristic
principles.
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