Optimal Database Marketing: Strategy, Development, and Data Mining
Optimal Database Marketing: Strategy, Development, and Data Mining
Toward Multidatabase Mining: Identifying Relevant Databases
IEEE Transactions on Knowledge and Data Engineering
Database classification for multi-database mining
Information Systems
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining Customer Value: From Association Rules to Direct Marketing
Data Mining and Knowledge Discovery
Strategic Database Marketing
Applying knowledge engineering techniques to customer analysis in the service industry
Advanced Engineering Informatics
Synthesizing heavy association rules from different real data sources
Pattern Recognition Letters
Redundant association rules reduction techniques
International Journal of Business Intelligence and Data Mining
Data-mining application for country segmentation based on the RFM model
International Journal of Data Analysis Techniques and Strategies
Deriving strong association mining rules using a dependency criterion, the lift measure
International Journal of Data Analysis Techniques and Strategies
An efficient data mining approach for discovering interesting knowledge from customer transactions
Expert Systems with Applications: An International Journal
Efficient classification from multiple heterogeneous databases
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Normalised support: a virtual angle of measurement of 'interestingness'
International Journal of Data Analysis Techniques and Strategies
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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.