VGEN: Fast Vertical Mining of Sequential Generator Patterns

VGEN: Fast Vertical Mining of Sequential Generator Patterns

Paper in proceedings (conference paper)

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 generatorsis 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 namedVGEN (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.

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 generatorsis 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 namedVGEN (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.

sequential patterns, generators, vertical mining, candidate pruning

sequential patterns, generators, vertical mining, candidate pruning

FOURNIER-VIGER, P.; GOMARIZ, A.; ŠEBEK, M.; HLOSTA, M.

2015

02.09.2014

Springer Verlag

Munich

978-3-319-10159-0

Data Warehousing and Knowledge Discovery

476

488

12

http://dx.doi.org/10.1007/978-3-319-10160-6_42

VGEN___Sequential_Pattern_Generators