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

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.

Automated summary

Data mining has become popular, but traditional methods are not sufficient with increasing data. Soft computing algorithms are used for mathematical optimization to obtain better results in less time. This paper proposes a framework for rule mining using a soft computing algorithm, specifically the Grouped-Artificial Bee Colony Optimization (G-ABC). The algorithm selects relevant attributes, verifies features, and applies mean-variance optimization and neural-based deep learning to validate the outcome.