Data Mining and Machine Learning Applications

11Developing Decision Making and Risk Mitigation: Using CRISP-Data Mining

Vivek Parganiha¹*, Soorya Prakash Shukla² and Lokesh Kumar Sharma³

¹Department of Computer Science &Engineering, Bhilai Institute of Technology, Durg, India

²Department of Electrical Engineering, Bhilai Institute of Technology, Durg, India

³ICMR-National Institute of Occupational Health, Department of Health Research, Ministry of Health and Family Welfare, Ahmedabad, India

Abstract

In this chapter, we will examine the usage of CRISP-DM philosophy in an ERP framework, which contains gigantic measures of the Information associated with the actual implementation of the business steps. Such systems get a particular method of tracking activities that leads to a confusing description of business measures in opportunity reports. A few works have been led on ERP frameworks, the greater part of them zeroing in on the improvement of new calculations for the programmed revelation of business measures. We zeroed in on tending to issues like, in what capacity can associations with ERP frameworks apply measure mining for breaking down their business measures to improve them. CRISP-DM had already emerged as that of the agreed standard for the development of data analytics and data Discovery Projects. Productive Data Mining involves three classes of exposure capabilities in a specific incident, sequence and determination. The Data operator uses more than a single testing technique to secure optimum performance. The goal ...