CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns Carson Kai-Sang Leung, Quamrul I. Khan, and Tariqul Hoque   View Paper (PDF)   Return to Session 13: Learning Techniques II Abstract Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree based frequent-pattern mining. In this paper, we propose a novel tree structure, called CanTree (Canonical-order Tree), that captures the content of the transaction database and orders tree nodes according to some canonical order. By exploiting its nice properties, the CanTree can be easily maintained when database transactions are inserted, deleted, and/or modified. For example, the CanTree does not require adjustment, merging, and/or splitting of tree nodes during maintenance. No rescan of the entire updated database or reconstruction of a new tree is needed for incremental updating. Experimental results show the effectiveness of our CanTree. ©2006 Association for Computing Machinery