OInduced: an efficient algorithm for mining induced patterns from rooted ordered trees

摘要

Frequent tree patterns have many practical applicationsin different domains such as XML mining, web usageanalysis, etc. In this paper, we present OInduced, a novel and efficient algorithm for finding frequent ordered induced tree patterns. OInduced uses a breadth-first candidate generation method and improves it by means of an indexing scheme. We also introduce frequency counting using tree encoding. For this purpose, we present two novel tree encodings, m-coding and cmcoding, and show how they can restrict nodes of input trees and compute frequencies of generated candidates. We perform extensive experiments on both real and synthetic datasets to showefficiency and scalability of OInduced.