Title :
An algorithm for mining generalized sequential patterns
Author :
Ren, Ju-Dong ; Cheng, Yin-Bo ; Yang, Lung-Lung
Author_Institution :
Modern Educ. & Technol. Center, Yanshan Univ., Qinhuangdao, China
Abstract :
Sequential pattern mining is an important data mining problem with broad applications. Algorithm GSP discovers generalized sequential patterns. However, GSP still encounters problems when a sequence database is large and/or when sequential patterns to be mined are long. Algorithm PrefixSpan mines complete sequential patterns faster than GSP but it cannot mine generalized sequential patterns with time constraints, time windows and/or taxonomy. In this paper, a new enhanced method based on PrefixSpan, is proposed, called EPSpan, which absorbs the spirit of PrefixSpan and extends PrefixSpan towards mining generalized sequential patterns.
Keywords :
data mining; sequences; very large databases; data mining problem; sequence database; sequential pattern mining; Data engineering; Data mining; Databases; Educational institutions; Educational technology; Electronic mail; Information science; Itemsets; Taxonomy; Time factors;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1382391
