A metaheuristic approach for mining gradual patterns

Swarm intelligence is a discipline that studies the collective behavior that is produced by local interactions of a group of individuals with each other and with their environment. In Computer Science domain, numerous swarm intelligence techniques are applied to optimization problems that seek to efficiently find best solutions within a search space. Gradual pattern mining is another Computer Science field that could benefit from the efficiency of swarm based optimization techniques in the task of finding gradual patterns from a huge search space. A gradual pattern is a rule-based correlation that describes the gradual relationship among the attributes of a data set. For example, given attributes {G, H} of a data set a gradual pattern may take the form: the less G, the more H(simply denoted as {G down arrow, H up arrow}). In this paper, we propose a numeric encoding for gradual pattern candidates that we use to define an effective search space. In addition, we present a systematic study of several meta-heuristic optimization techniques as efficient solutions to the problem of finding gradual patterns using our search space.

Automated summary

This paper introduces the research of swarm intelligence and gradual pattern mining, and proposes a numeric encoding method for gradual pattern candidates. Several meta-heuristic optimization techniques are applied to efficiently solve the problem of finding gradual patterns using the defined search space.