Research Article
Mining Robust Overlapping Co-Clustering in the Presence of Noise
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 117 - Issue 13 |
| Published: May 2015 |
| Authors: P. Sudhakar, S. Saranya |
10.5120/20618-3322
|
P. Sudhakar, S. Saranya . Mining Robust Overlapping Co-Clustering in the Presence of Noise. International Journal of Computer Applications. 117, 13 (May 2015), 40-44. DOI=10.5120/20618-3322
@article{ 10.5120/20618-3322,
author = { P. Sudhakar,S. Saranya },
title = { Mining Robust Overlapping Co-Clustering in the Presence of Noise },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 117 },
number = { 13 },
pages = { 40-44 },
doi = { 10.5120/20618-3322 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A P. Sudhakar
%A S. Saranya
%T Mining Robust Overlapping Co-Clustering in the Presence of Noise%T
%J International Journal of Computer Applications
%V 117
%N 13
%P 40-44
%R 10.5120/20618-3322
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Data clustering techniques have been applied to extract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great achievement. However, due to the various structures and intensive noise, there is no reliable clustering approach can be applied to all gene expression data. In this paper, the problem of revealing robust overlapping co-clustering is identified in the presence of noise. Instead of requiring all objects in a cluster have identical attribute order, this system requires that (1) at least a certain fraction of the objects have identical attribute order; (2) other objects in the cluster may deviate from the consensus order by up to a certain fraction of attributes.
References
- Chun Tang, Li Zhang, Aidong Zhang and Murali Ramanathan. Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis 2011.
- Jinze Liu and Wei Wang. OP-Cluster: Clustering by Tendency in High dimensional Space 2004.
- Amir Ben- Dor, Benny Chor, Richrd Karp and Zohar Yakhini . Discovering Local Structure In Gene Expression Data: The Order – Preserving Sub matrix Problem 2003.
- Haixun Wang, Wei Wang, Jiong Yang, Philip S. Yu. Clustering by Pattern Similarity in Large Data Sets 2007. Arabinda Panda, Satchidananda Dehuri. Biclustering for Microarray Data: A Short and Comprehensive Tutorial 2012.
- Lance Parsons, Ehtesham Hague and Huan Liu. Subspace Clustering for High Dimensional Data 2004.
- J. Z. Liu and W. Wang, "OP-Cluster: Clustering by tendency in high dimensional space", In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM), 2003, pp. 187-194.
- L. Bergroth, H. Hakonen and T. Raita, "A survey of longest common subsequence algorithms", SPIRE, pp 39-48, 2000.
- Spellman et al. , "Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization", Molecular Biology of the Cell 9, 1998, pp. 3273-3297.
- Fisher, R. A. (1922), "On the interpretation of ?2 from contingency tables, and the calculation of P", Journal of the Royal Statistical Society 85(1): pp. 87-94.
- L. Parsons, E. Haque, and H. Liu, "Subspace clustering for high dimensional data: a review", ACM SIGKDD Explorations Newsletter, Volume 6, Issue 1, 2004, pp. 90-105.
- Ben-Dor, B. Chor, R. M. Karp and Z. Yakhini, "Discovering local structure in gene expression data: the order-preserving submatrix problem", Journal of Computational Biology 10(3/4), 2003, pp. 373-384.
- Y. Cheng and G. Church, "Biclustering of expression data", In Proceedings of the 8th International Conference on Intelligent System for Molecular Biology, 2000.
- H. Wang, W. Wang, J. Yang and P. Yu, "Clustering by pattern similarity in Gene ontology consortium, www. geneontology. org.
Index Terms
Keywords