Efficient mining of maximal correlated weight frequent patterns

Maximal frequent pattern mining has been suggested for data mining to avoid generating a huge set of frequent patterns. Conversely, weighted frequent pattern mining has been proposed to discover important frequent patterns by considering the weighted support. We propose two mining algorithms of maximal correlated weight frequent pattern (MCWP), termed MCWP(WA) (based on Weight Ascending order) and MCWP(SD) (based on Support Descending order), to mine a compact and meaningful set of frequent patterns. MCWP(SD) obtains an advantage in conditional database access, but may not obtain the highest weighted item of the conditional database to mine highly correlated weight frequent patterns. Thus, we suggest a technique that uses additional conditions to prune lowly correlated weight items before the subsets checking process. Analyses show that our algorithms are efficient and scalable.