Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Discovering typical structures of documents: a road map approach
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Discovering Structural Association of Semistructured Data
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Representative Objects: Concise Representations of Semistructured, Hierarchial Data
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDT '97 Proceedings of the 6th International Conference on Database Theory
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium 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
W3QS: A Query System for the World-Wide Web
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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With the growing popularity of the World Wide Web, the number of semistructured documents produced in all types of organizations increases at a rapid rate. However the provided information cannot be queried or manipulated in the general way since, although there is some structure in the information, it is too irregular to be modeled using a relational or an object-oriented approach. Nevertheless, some semistructured objects, for the same type of information, have a very similar structure. In this paper we address the problem of finding such regularities and we propose a general architecture based on a very efficient data mining technique.