Mining important association rules based on the RFMD technique
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.
Volume (Year): 2 (2010)
Issue (Month): 1 ()
|Contact details of provider:|| Web page: http://www.inderscience.com/browse/index.php?journalID=282|
When requesting a correction, please mention this item's handle: RePEc:ids:injdan:v:2:y:2010:i:1:p:1-21. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Graham Langley)
If references are entirely missing, you can add them using this form.