Abstract

Data mining may be a computerized technology that uses complicated algorithms to seek out relationships and trends in large databases, real or perceived, previously unknown to the retailer, to market decision support. Data mining is predicted to be one of the widespread recognition of the potential for analysis of past transaction data to enhance the standard of future business decisions. The aim is to arrange a set of knowledge items and classify them. In this paper, we apply two classifier algorithms: J48 (c4.5) and Random Forest on the IRIS dataset, and we compare their performance based on different measures.