Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
A database perspective on knowledge discovery
Communications of the ACM
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Various Patterns in Sequential Data in an SQL-like Manner
ADBIS '99 Proceedings of the Third East European Conference on Advances in Databases and Information Systems
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
SQL-like language for database mining
ADBIS'97 Proceedings of the First East-European conference on Advances in Databases and Information systems
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The most popular data mining techniques consist in searching databases for frequently occurring patterns, e.g. association rules, sequential patterns. We argue that in contrast to today's loosely-coupled tools, data mining should be regarded as advanced database querying and supported by Database Management Systems (DBMSs). In this paper we descirbe our research prototype system, which logically extends DBMS functionality, offering extensive support for pattern discovery, storage and management. We focus on the system architecture and novel SQL-based data mining query language, which serves as the user interface to the system.