A database perspective on knowledge discovery
Communications of the ACM
Data mining: concepts and techniques
Data mining: concepts and techniques
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Free Itemsets under Constraints
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Optimizing subset queries: a step towards SQL-based inductive databases for itemsets
Proceedings of the 2004 ACM symposium on Applied computing
Index Support for Frequent Itemset Mining in a Relational DBMS
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Visual Analytics: A 2D-3D visualization support for human-centered rule mining
Computers and Graphics
Interactive visual exploration of association rules with rule-focusing methodology
Knowledge and Information Systems
Extending the UML for designing association rule mining models for data warehouses
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
| Hi-index | 0.00 |
Recently inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. With an IDB the user/analyst performs a set of very different operations on data using a special-purpose language, powerful enough to perform all the required manipulations, such as data preprocessing, pattern discovery and pattern post-processing. In this paper we present a comparison between query languages (MSQL, DMQL and MINE RULE) that have been proposed for association rules extraction in the last years and discuss their common features and differences. We present them using a set of examples, taken from the real practice of data mining. This allows us to define the language design guidelines, with particular attention to the open issues on IDBs.