Paper Info
Title
Intelligent sequential mining via alignment: optimization techniques for very large DB
Abstract
The shear volume of the results in traditional support based frequent sequential pattern mining methods has led to increasing interest in new intelligent mining methods to find more meaningful and compact results. One such approach is the consensus sequential pattern mining method based on sequence alignment, which has been successfully applied to various areas. However, the current approach to consensus sequential pattern mining has quadratic run time with respect to the database size limiting its application to very large databases. In this paper, we introduce two optimization techniques to reduce the running time significantly. First, we determine the theoretical bound for precision of the proximity matrix and reduce the time spent on calculating the full matrix. Second, we use a sample based iterative clustering method which allows us to use a much faster k-means clustering method with only a minor increase in memory consumption with negligible loss in accuracy.
Year
DOI
Venue
2007
10.1007/978-3-540-71701-0_62
PAKDD
Keywords
Field
DocType
optimization technique,quadratic run time,proximity matrix,intelligent sequential mining,current approach,frequent sequential pattern mining,new intelligent mining method,full matrix,consensus sequential pattern mining,iterative clustering method,large db,k-means clustering method,compact result,very large database,sequential pattern mining,sequence alignment,k means clustering
Data mining,Matrix (mathematics),Computer science,Quadratic equation,Sequential mining,Artificial intelligence,Cluster analysis,Sequential Pattern Mining,Machine learning,Limiting
Conference
Volume
ISSN
Citations 
4426
0302-9743
2
PageRank 
References 
Authors
0.38
6
3
Name
Order
Citations
PageRank
Hye-Chung Kum111412.99
Joong Hyuk Chang240119.81
Wei Wang37122746.33