Authors: Yoones Asgharzadeh Sekhavat, Mohammad Fathian, Mohammad Reza Gholamian, Somayeh Alizadeh
Addresses: Industrial Engineering Department, Iran University of Science and Technology, Narmak, Tehran, Iran. ' Industrial Engineering Department, Iran University of Science and Technology, Narmak, Tehran, Iran. ' Industrial Engineering Department, Iran University of Science and Technology, Narmak, Tehran, Iran. ' Industrial Engineering Department, Khaje Nasir Toosi University, Vanak, Tehran, Iran
Abstract: The method of association rule mining has been used by marketers for many years to extract marketing rules from purchase transactions. Marketers and managers employ these rules in order to predict customer needs for future sales. Extracting effective rules is one of the major problems of marketers. Effective rules can help them to make better marketing decisions. On the other hand, the Recency, Frequency, Monetary value and Duration (RFMD) method is one of the popular methods used in market segmentation that indicate profitable groups of customers. In this paper, a novel method is proposed that takes advantage of the RFMD method to extract effective association rules from profitable segments of purchase transactions. In other words, in the first step, raw data are classified based on the RFMD technique; and in the second step, effective association rules are extracted from sections with high RFMD values. The proposed method employs a new Maximum Frequent Itemset Extractor (MFIE) algorithm that outperforms the classic algorithm (Apriori) in extracting frequent itemsets from a large number of transactions. In addition, unlike most of the previous central methods, the proposed method is designed for extracting association rules from distributed databases.
Keywords: association rules; recency frequency monetary value duration; RFMD; maximum frequent itemset; data analysis; data mining; marketing rules; purchase transactions; market segmentation.
International Journal of Data Analysis Techniques and Strategies, 2010 Vol.2 No.1, pp.1 - 21
Available online: 03 Dec 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article