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
Optimized Substructure Discovery for Semi-structured Data
PKDD '02 Proceedings of the 6th 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
Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Canonical forms for labelled trees and their applications in frequent subtree mining
Knowledge and Information Systems
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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XML is going to be the main language for exchanging financial information between businesses over the Internet. As more and more banks and financial institutions move to electronic information exchange and reporting, the financial world is in a flood of information. With the sheer amount of financial information stored, presented and exchanged using XML-based standards, the ability to extract interesting knowledge from the data sources to better understand customer buying/selling behaviors and upward/downward trends in the stock market becomes increasingly important and desirable. Hence, there have been growing demands for efficient methods of discovering valuable information from a large collection of XML-based data. One of the most popular approaches to find the useful information is to mine frequently occurring tree patterns. In this paper, we propose a novel algorithm, FIXiT,for efficiently extracting maximal frequent subtrees from a set of XML-based documents. The main contributions of our algorithm are that: (1) it classifies the available financial XML standards such as FIXML, FpML, XBRL, and so forth with respect to their specifications, and (2) there is no need to perform tree join operations during the phase of generating maximal frequent subtrees.