Fast Mining RFM Patterns for Behavioral Analytics

In recent years, the problem of high-utility itemset mining (HUIM) has been extensively studied. However, HUIM algorithms only reveal profitable but generalized itemsets from transaction databases. In the market analysis domain, these mining results just reflect the sales trend of all customers and are not sufficient for making market strategies. In other words, it is hard to maintain specific customers for a long time due to the limitations of HUIM analysis of customer behaviors. In this paper, a novel data mining algorithm called RFM-Miner is proposed to discover RFM-patterns that are highly recent, frequent, and profitable in transaction databases. The novel algorithm relies on the array-bin structure to fast calculate adopted upper-bounds (i.e., transaction-weighted utilization, subtree and local utility) in linear time and space. In addition, RFM-Miner always searches for extension items of an itemset in a small projected database. And the merging technique is utilized to reduce the size of the search space. An extensive experimental study on four datasets (including real-life and synthetic) shows that RFM-Miner performs very well in terms of runtime and memory consumption. The novel algorithm also achieves better performance than the state-of-the-art benchmarks, especially on dense datasets.