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Efficient and Accurate Discovery of Patterns in Sequence Data Sets
August 2011 (vol. 23 no. 8)
pp. 1154-1168
ASCII Text
x 
 
"Efficient and Accurate Discovery of Patterns in Sequence Data Sets," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 8, pp. 1154-1168, August, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2011.69,
author = {},
title = {Efficient and Accurate Discovery of Patterns in Sequence Data Sets},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {23},
number = {8},
issn = {1041-4347},
year = {2011},
pages = {1154-1168},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.69},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Efficient and Accurate Discovery of Patterns in Sequence Data Sets
IS - 8
SN - 1041-4347
SP1154
EP1168
EPD - 1154-1168
PY - 2011
KW - trees (mathematics)
KW - data mining
KW - suffix-tree-based algorithm
KW - sequence data sets
KW - sequence mining algorithms
KW - flexible and accurate motif detector
KW - FLAME
KW - Computational modeling
KW - Fires
KW - Data mining
KW - Biological system modeling
KW - Approximation algorithms
KW - DNA
KW - Proteins
KW - suffix tree.
KW - Motif
KW - sequence mining
VL - 23
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require efficient mining of “approximate” patterns that are contiguous. The few existing algorithms that can be applied to find such contiguous approximate pattern mining have drawbacks like poor scalability, lack of guarantees in finding the pattern, and difficulty in adapting to other applications. In this paper, we present a new algorithm called FLexible and Accurate Motif DEtector (FLAME). FLAME is a flexible suffix-tree-based algorithm that can be used to find frequent patterns with a variety of definitions of motif (pattern) models. It is also accurate, as it always finds the pattern if it exists. Using both real and synthetic data sets, we demonstrate that FLAME is fast, scalable, and outperforms existing algorithms on a variety of performance metrics. In addition, based on FLAME, we also address a more general problem, named extended structured motif extraction, which allows mining frequent combinations of motifs under relaxed constraints.

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Index Terms:
trees (mathematics),data mining,suffix-tree-based algorithm,sequence data sets,sequence mining algorithms,flexible and accurate motif detector,FLAME,Computational modeling,Fires,Data mining,Biological system modeling,Approximation algorithms,DNA,Proteins,suffix tree.,Motif,sequence mining
Citation:
"Efficient and Accurate Discovery of Patterns in Sequence Data Sets," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 8, pp. 1154-1168, Aug. 2011, doi:10.1109/TKDE.2011.69
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