Enhanced Frequent Pattern Growth Model for ERP System

Abstract

The present paper deals with the analysis of data mining influences on an ERP framework. Here, we have sought to use data mining techniques for estimating the optimum outcome for an enterprise's development and setup using the ERP information. Furthermore, we have suggested a model in this research that integrates the database, customer inquiries, transactions, and all other ERP system requirements, while data mining techniques are applied to integrate decision making and predict flows. We have acquired data from the central database in cluster format depending on the action done against the queries issued by the clients, leveraging ERP's attributes and environment. Moreover, Apriori Algorithm uses clustered data to obtain newer patterns and rules for the improvement of an enterprise.This indeed represents a total implementation of data mining techniques upon the ERP framework in order to anticipate the answers to future inquiries. It has the potential to  make the enterprises maintain better relationships with prospective consumers which will help them remain happy and contented at all times.