Paper Info
Title
A gradational reduction approach for mining sequential patterns
Abstract
The technology of data mining is more important in recent years, and it is generally applied to commercial forecast and decision supports. Sequential pattern mining algorithms in the field of data mining play one of the important roles. Many of sequential pattern mining algorithms were proposed to improve the efficiency of data mining or save the utility rate of memory. So, our major study tries to improve the efficiency of sequential pattern mining algorithms. We propose a new algorithm - GRS (A Gradational Reduction Approach for Mining Sequential Patterns) which is an efficient algorithm of mining sequential patterns. GRS algorithm uses gradational reduction mechanism to reduce the length of transactions and uses GraDec function to avoid generating large number of infrequent sequential patterns; and it is very suitable to mine the transactions of databases whose record lengths are very long. The GRS algorithm only generates some sequences which are very possible to be frequent. So, the GRS algorithm can decrease a large number of infrequent sequences and increase the utility rate of memory.
Year
DOI
Venue
2007
10.1007/978-3-540-73325-6_56
IEA/AIE
Keywords
Field
DocType
infrequent sequential pattern,data mining,gradational reduction approach,important role,large number,mining sequential pattern,new algorithm,efficient algorithm,utility rate,grs algorithm,sequential pattern mining algorithm,sequential pattern mining,decision support,algorithm
Data mining,Computer science,FSA-Red Algorithm,Artificial intelligence,Sequential Pattern Mining,Machine learning
Conference
Volume
ISSN
Citations 
4570
0302-9743
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Jen-Peng Huang1576.45
Guo-Cheng Lan233219.45
Huang-Cheng Kuo34223.87