An introduction to database systems: vol. I (4th ed.)
An introduction to database systems: vol. I (4th ed.)
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Data mining: concepts and techniques
Data mining: concepts and techniques
A First Course in Database Systems
A First Course in Database Systems
Relational Data Mining
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining and Knowledge Discovery in Databases: Implications for Scientific Databases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
A Dynamic Web Service based Data Mining Process System
CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Efficient Classification across Multiple Database Relations: A CrossMine Approach
IEEE Transactions on Knowledge and Data Engineering
CoMMA: a framework for integrated multimedia mining using multi-relational associations
Knowledge and Information Systems
Spatial associative classification: propositional vs structural approach
Journal of Intelligent Information Systems
Multi-relational Association Rule Mining with Guidance of User
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
An approach to mining the multi-relational imbalanced database
Expert Systems with Applications: An International Journal
Multirelational classification: a multiple view approach
Knowledge and Information Systems
Extended MRI-Cube Algorithm for Mining Multi-Relational Patterns
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
A relational approach to probabilistic classification in a transductive setting
Engineering Applications of Artificial Intelligence
ILP-based concept discovery in multi-relational data mining
Expert Systems with Applications: An International Journal
Using trees to mine multirelational databases
Data Mining and Knowledge Discovery
Mining least relational patterns from multi relational tables
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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The multi relational data mining is one of the latest topics in data mining to find the relational patterns. In this paper, we have presented an algorithm for multi-relational rule mining using association rule mining and the optimization process. As a result of the association rule mining on the multirelational data, a number of relevant and irrelevant rules are generated. A rule is specified as a relation between two data points in the dataset. So, an optimization should be done on the mining algorithm in order to get the most relevant rules. We have adapted the technique of genetic algorithm in order to optimize the mined multi relational association rules. The genetic algorithm is one of the best optimization algorithm available and it suites the current problem because of its particular features such as the genetic operators crossover and mutation. The optimization of the rule is done by altering the fitness function of the genetic algorithm in relation with the multi relational data mining algorithm. The results from the experimental analysis showed that the proposed approach has better efficiency over the previous approaches. The most rules optimized is 198 under iterations 10 with a support of 60.