Openly accessible

Data pre-processing for more effective gene clustering

Hou, Jingyu and Chen, Yi-Ping Phoebe 2009, Data pre-processing for more effective gene clustering, in Proceedings of the 2nd International Joint Conference on Computational Sciences and Optimization, CSO 2009, IEEE Computer Society, Los Alamitos, Calif., pp. 710-713.

Attached Files
Name Description MIMEType Size Downloads
hou-datapreprocessingpublished-2009.pdf Published version application/pdf 243.00KB 86

Title Data pre-processing for more effective gene clustering
Author(s) Hou, Jingyu
Chen, Yi-Ping Phoebe
Conference name International Joint Conference on Computational Sciences and Optimization (2nd : 2009 : Sanya, Hainan, China)
Conference location Sanya, Hainan, China
Conference dates 24-26 April 2009
Title of proceedings Proceedings of the 2nd International Joint Conference on Computational Sciences and Optimization, CSO 2009
Editor(s) Yu, Lean
Lai, K. K.
Mishra, S. K.
Publication date 2009
Conference series International Joint Conference on Computational Sciences and Optimization
Start page 710
End page 713
Total pages 2 v. : ill.
Publisher IEEE Computer Society
Place of publication Los Alamitos, Calif.
Keyword(s) Bioinformatics
Gene
Clustering
Data Processing
Data Mining
Summary The high-throughput experimental data from the new gene microarray technology has spurred numerous efforts to find effective ways of processing microarray data for revealing real biological relationships among genes. This work proposes an innovative data pre-processing approach to identify noise data in the data sets and eliminate or reduce the impact of the noise data on gene clustering, With the proposed algorithm, the pre-processed data sets make the clustering results stable across clustering algorithms with different similarity metrics, the important information of genes and features is kept, and the clustering quality is improved. The primary evaluation on real microarray data sets has shown the effectiveness of the proposed algorithm.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 9780769536057
Language eng
Field of Research 060102 Bioinformatics
080109 Pattern Recognition and Data Mining
Socio Economic Objective 810105 Intelligence
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
HERDC collection year 2009
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30020774

Document type: Conference Paper
Collections: School of Information Technology
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Access Statistics: 408 Abstract Views, 99 File Downloads  -  Detailed Statistics
Created: Fri, 30 Oct 2009, 10:22:43 EST by Jingyu Hou

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.