Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An evolutionary algorithm to discover numeric association rules
Proceedings of the 2002 ACM symposium on Applied computing
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Expert Systems with Applications: An International Journal
Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Discovering gene association networks by multi-objective evolutionary quantitative association rules
Journal of Computer and System Sciences
Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Association rules are one of the most frequently used tools for finding relationships between different attributes in a database. There are various techniques for obtaining these rules, the most common of which are those which give categorical association rules. However, when we need to relate attributes which are numeric and discrete, we turn to methods which generate quantitative association rules, a far less studied method than the above. In addition, when the database is extremely large, many of these tools cannot be used. In this paper, we present an evolutionary tool for finding association rules in databases (both small and large) comprising quantitative and categorical attributes without the need for an a priori discretization of the domain of the numeric attributes. Finally, we evaluate the tool using both real and synthetic databases.