Mining Sequential Patterns with Item Constraints

Abstract

Mining sequential patterns is to discover sequential purchasing behaviors for most customers from a large amount of customer transactions. Past transaction data can be analyzed to discover customer purchasing behaviors. However, the size of the transaction database can be very large. It is very time consuming to find all the sequential patterns from a large database, and users may be only interested in some items. Moreover, the criteria of the discovered sequential patterns for the user requirements may not be the same. Many uninteresting sequential patterns 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 sequential patterns to be discovered. Also, an efficient data mining technique is proposed to extract the sequential patterns according to the users’ requests.