Mining interesting sequential patterns for intelligent systems

• Published in: International journal of intelligent systems Vol. 20; no. 1; pp. 73 - 87
• Main Author: Yen, Show-Jane
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.01.2005; Wiley
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Information systems. Data bases; Learning and adaptive systems; Memory organisation. Data processing; Software; Data analysis; Database query; Query; Very large databases; Database; Information extraction; Transaction processing; Data mining; Intelligent system
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1002/int.20054

Mining sequential patterns means to discover sequential purchasing behaviors of most customers from a large number of customer transactions. Past transaction data can be analyzed to discover customer purchasing behaviors such that the quality of business decisions can be improved. 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 sequential patterns. Moreover, the criteria of the discovered sequential patterns for user requirements may not be the same. Many uninteresting sequential patterns for 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 knowledge of interest to them from a large database of customer transactions. In this article, a data mining language is presented. From the data mining language, users can specify the items of interest 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. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 73–87, 2005.