PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Data on the Web: from relations to semistructured data and XML
Data on the Web: from relations to semistructured data and XML
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Mining Closed and Maximal Frequent Subtrees from Databases of Labeled Rooted Trees
IEEE Transactions on Knowledge and Data Engineering
Canonical forms for labelled trees and their applications in frequent subtree mining
Knowledge and Information Systems
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
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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Recently, XML is penetrating virtually all areas of computer science and information technology, and is bringing about an unprecedented level of data exchange among heterogeneous data storage systems. With the continuous growth of online information stored, presented and exchanged using XML, the discovery of useful information from a collection of XML documents is currently one of the main research areas occupying the data mining community. The mostly used approach to this task is to extract frequently occurring subtree patterns in trees. However, the number of frequent subtrees usually grows exponentially with the size of trees, and therefore, mining all frequent subtrees becomes infeasible for a large tree size. A more practical and scalable approach is to use maximal frequent subtrees, the number of which is much smaller than that of frequent subtrees. Handling the maximal frequent subtrees is an interesting challenge, and represents the core of this paper. We present a novel, conceptually simple, yet effective approach that discovers maximal frequent subtrees without generation of candidate subtrees from a database of XML trees. The beneficial effect of our approach is that it not only reduces significantly the number of rounds for infrequent tree pruning, but also eliminates totally each round for candidate generation by avoiding time consuming tree join operations or tree enumerations.