AMIOT: Induced Ordered Tree Mining in Tree-structured Databases Shohei Hido and Hiroyuki Kawano   View Paper (PDF)   Return to Session 20: Data Mining Applications Abstract Frequent subtree mining has become increasingly important in recent years. In this paper, we present AMIOT algorithm to discover all frequent ordered subtrees in a tree-structured database. In order to avoid the generation of infrequent candidate trees, we propose the techniques such as right-and-left tree join and serial tree extension. Proposed methods enumerate only the candidate trees with high probability of being frequent without any duplications. The experiments on synthetic dataset and XML database show that AMIOT reduces redundant candidate trees and outperforms FREQT algorithm by up to five times in execution time. ©2006 Association for Computing Machinery