On query languages for the P-string data model
Information modelling and knowledge bases
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
TreeFinder: a First Step towards XML Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Data Mining for Maximal Frequent Subtrees
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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 Frequent Induced Subtrees by Prefix-Tree-Projected Pattern Growth
WAIMW '06 Proceedings of the Seventh International Conference on Web-Age Information Management Workshops
A new tree inclusion algorithm
Information Processing Letters
EFoX: a scalable method for extracting frequent subtrees
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
EXiT-B: a new approach for extracting maximal frequent subtrees from XML data
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
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Due to its highly flexible tree structure, XML data is used to capture most kinds of data and provides a substrate in which almost any other data structure may be presented. With the continuous growth of XML tree data in electronic environments, the discovery of useful knowledge from them has been a main research area in the information retrieval community. The mostly used approach to this task is to extract frequently occurring subtree patterns from a set of trees. However, because the number of frequent subtrees grows exponentially with the size of trees, a more practical and scalable alternative is required, which is the discovery of maximal frequent subtrees. The maximal frequent subtrees hold all the useful information, though, the number of them is much smaller than that of frequent subtrees. Handling the maximal frequent subtrees is an interesting challenge, and represents the core of this paper. As far as we know, this is one of the first studies to directly discover maximal frequent subtrees without any candidate sets generations as well as eliminating the process of useless subtree pruning. To this end, we define and use a new type of projected database to represent XML tree data efficiently. It significantly improves the entire process of mining maximal frequent subtree patterns. We study the performance and the scalability of the proposed approach through experiments based on synthetic datasets.