Paper Info
Title
A new approach for mining deep order-preserving submatrices
Abstract
In this paper, we proposed an exact method to discover all order-preserving submatrices (OPSMs) based on frequent sequential pattern mining. Firstly, an existing algorithm calACS is adjusted to disclose all common subsequences between every two row sequences, therefore all the deep OPSMs corresponding to long patterns with few supporting sequences will not be missed. Then an improved data structure for prefix tree was used to store and traverse all common subsequences, and Apriori principle was employed to mine the frequent sequential pattern efficiently. Finally, experiments were implemented on real data set and GO analysis was applied to identify whether the patterns discovered were biologically significant. The results demonstrate the effectiveness and the efficiency of this method.
Year
DOI
Venue
2014
10.1109/FSKD.2014.6980857
FSKD
Keywords
Field
DocType
improved data structure,apriori principle,frequent sequential pattern mining,trees (mathematics),the prefix tree,data structures,matrix algebra,calacs algorithm,data structure,biclustering,prefix tree,all common subsequences,opsm,data mining,frequent sequence,go analysis,deep order-preserving submatrix mining,bioinformatics,gene expression
Data structure,Data mining,Pattern recognition,Computer science,A priori and a posteriori,FSA-Red Algorithm,Atmospheric measurements,Artificial intelligence,Sequential Pattern Mining,Trie,Block matrix,Traverse
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Zhengling Liao153.44
Jie Luo270673.44
Meihang Li383.83
Yun Xue465.30
TieChen Li583.57
Xiao-Hui Hu6105.55