Enhanced Index-Based GenMax for Frequent Item Set Mining

Literature Review And Problem Statement

Mining of association rules from large data sets has been a focused topic in recent data mining research (Grahne and J. Zhu, 2004). To explore multiple-level association rule mining, (Leung et al., 2002) one needs to provide i.e., data at multiple levels of abstraction, and efficient methods for multiple-level rule mining. There are several possible directions to explore efficient mining of multiple-level association rules (Liu et al., 2003). The fundamental problem in item set mining is formulated as follows: Many of the proposed itemset mining algorithms are a variant of Apriori (Faloutsos et al., 1995), which employs a bottom-up, breadth-first search that enumerates every single frequent itemset. In many applications (especially in dense data) with long frequent patterns enumerating all possible 2m-2 subsets of a m length pattern (m can easily be 30 or 40 or longer) is computationally unfeasible (Baralis et al., 2005).