Algorithms on Trees and Graphs
Algorithms on Trees and Graphs
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
LOGML: Log Markup Language for Web Usage Mining
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
TreeFinder: a First Step towards XML Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Canonical forms for labelled trees and their applications in frequent subtree mining
Knowledge and Information Systems
Mining Substructures in Protein Data
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Tree model guided candidate generation for mining frequent subtrees from XML documents
ACM Transactions on Knowledge Discovery from Data (TKDD)
IMB3-Miner: mining induced/embedded subtrees by constraining the level of embedding
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Efficiently Mining Frequent Embedded Unordered Trees
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Model guided algorithm for mining unordered embedded subtrees
Web Intelligence and Agent Systems
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In this paper we present an algorithm for mining of unordered embedded subtrees. This is an important problem for association rule mining from semi-structured documents, and it has important applications in many biomedical, web and scientific domains. The proposed U3 algorithm is an extension of our general tree model guided (TMG) candidate generation framework and it considers both transaction based and occurrence match support. Synthetic and real world data sets are used to experimentally demonstrate the efficiency of our approach to the problem, and the flexibility of our general TMG framework.