A Metaheuristic Approach for Mining Gradual Patterns

TL;DR

This paper introduces a metaheuristic approach utilizing swarm intelligence techniques to efficiently mine gradual patterns from large datasets, enhancing the search process through numeric encoding and systematic optimization studies.

Contribution

It proposes a numeric encoding for gradual pattern candidates and systematically evaluates various metaheuristic algorithms for efficient pattern mining.

Findings

Effective search space defined by numeric encoding

Metaheuristic techniques outperform traditional methods

Systematic comparison of optimization algorithms

Abstract

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". 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.