Mining Valuable Fuzzy Patterns via the RFM Model

Yanlin Qi, Fuyin Lai, Guoting Chen, Wensheng Gan

Abstract: This paper aims to propose an effective algorithm to discover valuable patterns by applying the fuzzy method to the RFM model. RFM analysis is a common method in customer relationship management, through which we can identify valuable customer groups. By combining RFM analysis with frequent pattern mining, valuable RFM - patterns can be found from the RFM-pattern-tree, such as the RFMP-growth algorithm. Aiming to mine patterns that have quantitative relationships among items, we introduce the fuzzy method in the RFM model, and we present a fuzzy - Rfu - tree algorithm in which a new pruning strategy is proposed to prune candidate patterns. Experiments show the effectiveness of the new algorithm. The new algorithm guarantees a high overlap degree with the RFM-patterns gen-erated by RFMP-growth, with more valuable information (with additional fuzzy level) in the mined patterns.