VGEN: Fast Vertical Mining of Sequential Generator Patterns

• Published in: Data Warehousing and Knowledge Discovery pp. 476 - 488
• Main Authors: Fournier-Viger, Philippe, Gomariz, Antonio, Šebek, Michal, Hlosta, Martin
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2014
• Series: Lecture Notes in Computer Science
• Subjects: candidate pruning; generators; sequential patterns; vertical mining
• ISBN: 3319101595; 9783319101590
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-10160-6_42

Sequential pattern mining is a popular data mining task with wide applications. However, the set of all sequential patterns can be very large. To discover fewer but more representative patterns, several compact representations of sequential patterns have been studied. The set of sequential generators is one the most popular representations. It was shown to provide higher accuracy for classification than using all or only closed sequential patterns. Furthermore, mining generators is a key step in several other data mining tasks such as sequential rule generation. However, mining generators is computationally expensive. To address this issue, we propose a novel mining algorithm named VGEN (Vertical sequential GENerator miner). An experimental study on five real datasets shows that VGEN is up to two orders of magnitude faster than the state-of-the-art algorithms for sequential generator mining.