Trends in quantitative association rule mining techniques

Association rule mining (ARM) techniques are effective in extracting frequent patterns and hidden associations among data items in various databases. These techniques are widely used for learning behavior, predicting events and making decisions at various levels. The conventional ARM techniques are however limited to databases comprising categorical data only whereas the real-world databases mostly in business and scientific domains have attributes containing quantitative data. Therefore, an improvised methodology called Quantitative Association Rule Mining (QARM) is used that helps discovering hidden associations from the real-world quantitative databases. In this paper, we present an exhaustive discussion on the trends in QARM research and further make a systematic classification of the available techniques into different categories based on the type of computational methods they adopted. We perform a critical analysis of various methods proposed so far and present a theoretical comparative study among them. We also enumerate some of the issues that needs to be addressed in future research.