Title
FMGSP: An Efficient Method of Mining Global Sequential Patterns
Abstract
Now some distributed sequential patterns mining algorithms generate too many candidate sequences, and increase communication overhead. Therefore, we propose an efficient algorithm-FMGSP (fast mining of global sequential patterns) of mining global sequential pattern on distributed system. Our method of mining sequential pattern in distributed environment differs from previous related works. Two main contributions are made in this paper. First local sequential patterns obtained on every site in distributed environment are compressed into a lexicographic sequence tree before all subtrees will be distributed into polling site, Second, an efficient pruning strategy called I/S-EP (item and sequence extension pruning) is proposed to reduce candidate sequences. Just this, the cost of communication in the network is reduced greatly when counting requests are sent (or received) to the corresponding databases. Both theories and experiments indicate that the performance of FMGSP is predominant for large databases, the global sequential patterns could be obtained effectively by the method after reducing the cost of communication.
Year
DOI
Venue
2007
10.1109/FSKD.2007.294
FSKD (2)
Keywords
Field
DocType
item extension pruning,corresponding databases,sequential patterns mining algorithm,sequence extension pruning,increase communication overhead,trees (mathematics),global sequential pattern,distributed sequential pattern mining,fmgsp,efficient algorithm-fmgsp,global sequential pattern mining,mining sequential pattern,efficient pruning strategy,local sequential pattern,candidate sequence,data mining,efficient method,large databases,lexicographic sequence tree,distributed processing,mining global sequential patterns,distributed environment,sequential pattern mining,distributed system
Data mining,Pattern recognition,Distributed Computing Environment,Computer science,Polling,Artificial intelligence,Lexicographical order,Machine learning,Pruning
Conference
Volume
ISBN
Citations 
2
978-0-7695-2874-8
2
PageRank 
References 
Authors
0.38
6
5
Name
Order
Citations
PageRank
Changhai Zhang11098.37
Kongfa Hu2389.26
Haidong Liu320.38
Youwei Ding421.05
Ling Chen5426.49