Classification in data mining using POEMs/GA algorithm
Tanmay Singha, Saubhik Goswami · 2017
Data mining is one of the most emerging research domains in the recent decades in order to extract implicit and useful knowledge from the large database. It is the process of analyzing data from the different perspectives and summarizing it into useful information. It plays an important role to extract the hidden business intelligence from the large amount of data. In general, a large database contains an enormous number of elements, making an exhaustive search is infeasible. Therefore, efficient search strategies are needed to extract useful knowledge from large database. In this paper, we propose a novel approach in order to discover classification rules for data mining. This approach is based on Iterative Prototypes Optimization with Evolved iMprovement (POEMs) algorithm. It is an evolutionary algorithm which uses a genetic algorithm (GA) as an optimization technique for resetting the parameters in the classifier. The goal of this paper is to develop an effective evolutionary classifier which can be effectively used to extract the hidden patterns from the large data sets.