Grouped ABC for Feature Selection and Mean-Variance Optimization for Rule Mining: A Hybrid Framework

Abstract

Data mining has become a popular process in recent times. However, with the increase in data, traditional data mining methods are not sufficient to solve many problems. Therefore, advanced techniques are needed to provide better results without consuming more time during execution. Soft computing algorithms are used for mathematical optimization to achieve better results in less time. The primary purpose of this work is to propose a framework for rule mining that shall generalize the currently applied methods in rule mining. In this respect, this paper represents the R-miner using a soft computing algorithm. The Grouped -Artificial Bee Colony Optimization (G-ABC) was used to select the relevant attribute set and further verify the features. Mean-Variance optimization is used to find whether the selected rule is valid for further classification. Furthermore, a neural-based deep learning method is applied to validate the outcome. The investigation outcome indicates that the proposed algorithm provides more optimized results in terms of the number of rules generated, the time required for calculation, and obtaining supplementary information for rule mining.

Keywords

• Rule mining
• feature selection
• particle swarm optimization
• artificial bee colony optimization