Ant Colony Optimization for Mining Gradual Patterns

TL;DR

This paper introduces an ant colony optimization method for mining gradual patterns in data, demonstrating improved performance over existing algorithms, especially on large datasets.

Contribution

The paper presents a novel ant colony optimization approach for extracting frequent gradual patterns in data mining.

Findings

The proposed algorithm outperforms existing methods on real-world datasets.

It is particularly effective with large data sets.

Computational experiments validate the efficiency of the approach.

Abstract

Gradual pattern extraction is a field in (KDD) Knowledge Discovery in Databases that maps correlations between attributes of a data set as gradual dependencies. A gradual dependency may take a form of "the more Attribute K , the less Attribute L". In this paper, we propose an ant colony optimization technique that uses a probabilistic approach to learn and extract frequent gradual patterns. Through computational experiments on real-world data sets, we compared the performance of our ant-based algorithm to an existing gradual item set extraction algorithm and we found out that our algorithm outperforms the later especially when dealing with large data sets.