Rare association rule mining: a systematic review
Anindita Borah, Bhabesh Nath · International Journal of Knowledge Engineering and Data Mining · 2017
One of the indispensable tasks of data mining is the extraction of significant and meaningful association rules. Whereas the extraction of frequent patterns using association rule mining is an imperative field of research, the idea of generating patterns that do not appear frequently in a database has grabbed the attention of researchers in recent years. The infrequent items or more commonly known as the rare items represent unknown or unpredictable associations and are therefore more interesting than the frequent ones. This study aims to provide a broad systematic review of the area of rare association rule mining. In this paper, a methodical analysis of the rare itemset and rare rule generation techniques in static and dynamic environment is presented. This paper also attempts to feature the current status and future perspectives of rare association rule mining along with some major research challenges.