Paper Info
Title
Generalization of pattern-growth methods for sequential pattern mining with gap constraints
Abstract
The problem of sequential pattern mining is one of the several that has deserved particular attention on the general area of data mining. Despite the important developments in the last years, the best algorithm in the area (Prefix-Span) does not deal with gap constraints and consequently doesn't allow for the introduction of background knowledge into the process. In this paper we present the generalization of the PrefixSpan algorithm to deal with gap constraints, using a new method to generate projected databases. Studies on performance and scalability were conducted in synthetic and real-life datasets, and the respective results are presented.
Year
DOI
Venue
2003
10.1007/3-540-45065-3_21
MLDM
Keywords
Field
DocType
prefixspan algorithm,last year,data mining,pattern-growth method,gap constraint,sequential pattern mining,general area,best algorithm,important development,new method,association rules
PrefixSpan,Data mining,Computer science,Sequential method,Information extraction,Artificial intelligence,Sequential Pattern Mining,Machine learning,Scalability
Conference
Volume
ISSN
ISBN
2734
0302-9743
3-540-40504-6
Citations 
PageRank 
References 
35
1.37
6
Authors
2
Name
Order
Citations
PageRank
Cláudia Antunes116116.57
Arlindo L. Oliveira22022120.06