An Effective Boolean Algorithm for Mining Association Rules in Large Databases
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
From Knowledge Management Concepts Toward Software Engineering Practices
PROFES '02 Proceedings of the 4th International Conference on Product Focused Software Process Improvement
Knowledge Discovery with the Associative Memory Modell Neunet
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
An efficient data mining approach for discovering interesting knowledge from customer transactions
Expert Systems with Applications: An International Journal
A method based on rough set for mining multi-dimensional association rules
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Generalized association rule mining using an efficient data structure
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Mining association rules is an important task. Past transaction data can be analyzed to discover customer purchasing behaviors such that the quality of business decision can be improved. The association rules describe the associations among items in the large database of customer transactions. However, the size of the database can be very large. It is very time consuming to find all the association rules from a large database, and users may be only interested in the associations among some items. Moreover, the criteria of the discovered rules for the user requirements may not be the same. Many uninteresting association rules for the user requirements can be generated when traditional mining methods are applied. Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. In this paper, a data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the rules to be discovered. Also, an efficient data mining technique is proposed to extract the association rules according to the users` requests.