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Combining Logistic Regression Analysis and Association Rule Mining via MLR Algorithm

Authors:
Özge Yücel Kasap
Nevzat Ekmekçi
Utku Görkem Ketenci

Keywords: Logistic Regression; Logit; Association Rule Mining; Apriori; Ensemble Learning; Stacking; MLR

Abstract:
One of the keys in marketing is to recommend the right products to the right customers. This paper proposes a solution to this problem as a part of the development of a new data mining tool PROPCA (Proximus Optimum Canistro). The aim is to use logistic regression analysis and association rule mining together to make recommendations in marketing. An innovative approach in which combination of these two algorithms provides better results than algorithms used stand-alone is presented. While association rule mining searches all rules in the data set, logistic regression predicts a purchase probability of a product for customers. The combination of these two approaches are tested on a real-life banking data set. The results of combination are shown and their suitability in general is discussed.

Pages: 154 to 159

Copyright: Copyright (c) IARIA, 2016

Publication date: August 21, 2016

Published in: conference

ISSN: 2308-4235

ISBN: 978-1-61208-498-5

Location: Rome, Italy

Dates: from August 21, 2016 to August 25, 2016