Mining Interesting Association Rules: A Data Mining Language

Mining association rules is to discover customer purchasing behaviors from a transaction database, such that the quality of business decision can be improved. However, the size of the transaction 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 some information. Moreover, the criteria of the discovered association rules for the user requirements may not be the same. Many uninteresting information 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 association rules to be discovered. Also, the efficient data mining techniques are proposed to extract the association rules according to the user requirements.