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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
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 you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.