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Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE'05)
Using Data Mining Techniques to Learn Layouts of Flat-File Biological Datasets
Minneapolis, Minnesota
October 19-October 21
ISBN: 0-7695-2476-1
ASCII Text
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Kaushik Sinha, Xuan Zhang, Ruoming Jin, Gagan Agrawal, "Using Data Mining Techniques to Learn Layouts of Flat-File Biological Datasets," Bioinformatic and Bioengineering, IEEE International Symposium on, pp. 177-184, Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE'05), 2005.
 
 
BibTex
x
 
@article{ 10.1109/BIBE.2005.56,
author = {Kaushik Sinha and Xuan Zhang and Ruoming Jin and Gagan Agrawal},
title = {Using Data Mining Techniques to Learn Layouts of Flat-File Biological Datasets},
journal ={Bioinformatic and Bioengineering, IEEE International Symposium on},
volume = {0},
year = {2005},
isbn = {0-7695-2476-1},
pages = {177-184},
doi = {http://doi.ieeecomputersociety.org/10.1109/BIBE.2005.56},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Bioinformatic and Bioengineering, IEEE International Symposium on
TI - Using Data Mining Techniques to Learn Layouts of Flat-File Biological Datasets
SN - 0-7695-2476-1
SP177
EP184
A1 - Kaushik Sinha,
A1 - Xuan Zhang,
A1 - Ruoming Jin,
A1 - Gagan Agrawal,
PY - 2005
KW - null
VL - 0
JA - Bioinformatic and Bioengineering, IEEE International Symposium on
ER -
 
Kaushik Sinha, Ohio State University
Xuan Zhang, Ohio State University
Ruoming Jin, Ohio State University
Gagan Agrawal, Ohio State University

One of the major problems in biological data integration is that many data sources are stored as flat-files, with a variety of different layouts. Integrating data from such sources can be an extremely time-consuming task.

We have been developing data mining techniques to help learn the layout of a dataset in a semi-automatic way. In this paper, we focus on the problem of identifying delimiters for optional fields. Since these fields do not occur in every record, frequency based methods are not able to identify the corresponding delimiters. We present a method which uses contrast analysis on the frequency of sequences to identify such delimiters and help complete the layout descriptions. We demonstrate the effectiveness of this technique using three flat-file biological datasets.

Citation:
Kaushik Sinha, Xuan Zhang, Ruoming Jin, Gagan Agrawal, "Using Data Mining Techniques to Learn Layouts of Flat-File Biological Datasets," bibe, pp.177-184, Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE'05), 2005
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