PC_Tree: Prime-Based and Compressed Tree for Maximal Frequent Patterns Mining

Knowledge discovery or extracting knowledge from large amount of data is a desirable task in competitive businesses. Data mining is an essential step in knowledge discovery process. Frequent patterns play an important role in data min- ing tasks such as clustering, classification, and prediction and association analysis. However, the mining of all frequent patterns will lead to a massive number of pat- terns. A reasonable solution is identifying maximal frequent patterns which form the smallest representative set of patterns to generate all frequent patterns. This research proposes a new method for mining maximal frequent patterns. The method includes an efficient database encoding technique, a novel tree structure called PC Tree and PC Miner algorithm. Experiment results verify the compactness and performance.