CP-tree: A tree structure for single-pass frequent pattern mining

Abstract

FP-growth algorithm using FP-tree has been widely studied for frequent pattern mining because it can give a great performance improvement compared to the candidate generation-and-test paradigm of Apriori. However, it still requires two database scans which are not applicable to processing data streams. In this paper, we present a novel tree structure, called CP-tree (Compact Pattern tree), that captures database information with one scan (Insertion phase) and provides the same mining performance as the FP-growth method (Restructuring phase) by dynamic tree restructuring process. Moreover, CP-tree can give full functionalities for interactive and incremental mining. Extensive experimental results show that the CP-tree is efficient for frequent pattern mining, interactive, and incremental mining with single database scan. © 2008 Springer-Verlag Berlin Heidelberg.