Efficient data mining technique for discovering interesting association rules

Abstract

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.