Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
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
Advances in knowledge discovery and data mining
Database Mining: A Performance Perspective
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
Discovery of Multiple-Level Association Rules from Large Databases
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
Mining Generalized Association Rules
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
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Materialized Data Mining Views
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Data Access Paths for Frequent Itemsets Discovery
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
Data Mining Support in Database Management Systems
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Distributed Data Mining Methodology with Classification Model Example
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Incremental association rule mining using materialized data mining views
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Incremental data mining using concurrent online refresh of materialized data mining views
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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Datamining, also referred to as databasemining or knowledge discovery in databases (KDD), is a new research area that aims at the discovery of useful information from large datasets. One of the most interesting and important research problems is discovering of different types of rules (e.g. association, characteristic, discriminant, etc.) from data. In this work we propose the new SQL-like language for datamining in relational databases, called MineSQL, developed within the scope of the data mining research project led in Poznan University of Technology. MineSQL is the extension of industry standard SQL language developed for expressing rule queries and assisting a user in rule generation, storage and retrieval. We focus on the main features of the language, its syntax and semantics, illustrated by practical examples.