The week's pick
Random Articles
Reseach Article
Effective Clustering Algorithms for Gene Expression Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 32 - Number 4 |
| Year of Publication: 2011 |
| Authors: T.Chandrasekhar, K.Thangavel, E.Elayaraja |
10.5120/3893-5454
|
T.Chandrasekhar, K.Thangavel, E.Elayaraja . Effective Clustering Algorithms for Gene Expression Data. International Journal of Computer Applications. 32, 4 ( October 2011), 25-29. DOI=10.5120/3893-5454
Abstract
Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in Bioinformatics research. In this paper, K-Means algorithm hybridised with Cluster Centre Initialization Algorithm (CCIA) is proposed Gene Expression Data. The proposed algorithm overcomes the drawbacks of specifying the number of clusters in the K-Means methods. Experimental analysis shows that the proposed method performs well on gene Expression Data when compare with the traditional K- Means clustering and Silhouette Coefficients cluster measure.
References
- A. M. Fahim, A. M. Salem, F. A. Torkey and M. A. Ramadan, 2006 “An Efficient enhanced K-Means clustering algorithm”, journal of Zhejiang University, 10 (7): 1626 - 1633.
- Bashar Al-Shboul and Sung-Hyon Myaeng, 2009 “Initializing K-Means using Genetic Algorithms”, World Academy of Science, Engineering and Technology 54.
- Chen Zhang and Shixiong Xia, 2009 “ K-Means Clustering Algorithm with Improved Initial center,” in Second International Workshop on Knowledge Discovery and Data Mining (WKDD), pp. 790-792.
- Chris Ding and Hanchuna Peng, 11-14 August - 2003 “Mininmum Redundancy Feature Selection from Microarray Gene Expression Data”, proceedings of the International Bioinformatic Conference.
- Edmond h.wuy, Michael K. Ngy, Andy M. Yipz, and Tony F. Chanz, 2004 “Discretization of Multidimensional Web Data forInformative Dense Regions Discovery”, CIS 2004, LNCS 3314, pp718-724.
- F. Yuan, Z. H. Meng, H. X. Zhangz, C. R. Dong, August 2004 “ A New Algorithm to Get the Initial Centroids”, proceedings of the 3rdInternational Conference on Machine Learning and Cybernetics, pp. 26-29.
- Doulaye Dembele and Philippe Kastner, 2003 “Fuzzy C means method for clustering microarray data”, Bioinformatics, Vol.19, no.8, pp.973- 980.
- Daxin Jiang, Jian Pei, and Aidong Zhang, October 2005 “An Interactive Approach to mining Gene Expression Data”. IEEE Transactions on Knowledge and Data Engineering, Vol 17, No.10, pp.1363- 1380.
- Dongxiao Zhu, Alfred O Hero, Hong Cheng, Ritu Khanna and Anand Swaroop, 2005 “Network constrained clustering for gene microarray Data”, doi:10.1093 /bioinformatics / bti 655, Vol. 21 no. 21, pp. 4014 – 4020,.
- Fahim A.M, Salem A. M, Torkey A and Ramadan M. A, 2006 “An Efficient enhanced K-Means clustering algorithm”, Journal of Zhejiang University, 10(7):1626–1633.
- K Karteeka Pavan, Allam Appa Rao, A V Dattatreya Rao, GR Sridhar, September 2008 “Automatic Generation of Merge Factor for Clustering Microarray Data”, IJCSNS International Journal of Computer Science and Network Security, Vol.8, No.9.
- Kohei Arai and Ali Ridho Barakbah, 2007 “Hierarchical K-Means: an algorithm for centroids initialization for K-Means”, Reports of the Faculty of Science and Engineering, Saga University, Vol. 36, No.1, 25-31.
- K.R De and A. Bhattacharya, 2008“Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying Patterns in expression profiles,” bioinformatics, Vol. 24, pp. 1359-1366.
- K. A. Abdul Nazeer and M. P. Sebastian, July 2009 “Improving the accuracy and efficiency of the K-Means clustering algorithm”, international Conference on Data Mining and Knowledge Engineering (ICDMKE), Proceedings of the World Congress on Engineering (WCE- 2009), London, UK. Vol 1.
- Lletí, R., Ortiz, M.C., Sarabia, L.A., Sánchez, M.S. 2004 “Selecting variables for K-Means cluster analysis by using a genetic algorithm that optimises the silhouettes”. Analytica Chimica Acta.
- Moh'd Belal Al- Zoubi and Mohammad al Rawi, 2008 “An Efficient Approach for Computing Silhouette Coefficients”. Journal of Computer Science 4 (3): 252-255.
- Madhu Yedla, Srinivasa Rao Pathakota, T M Srinivasa , 2010 “Enhancing K-Means Clustering Algorithm with Improved Initial Center” , Madhu Yedla et al. / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 1 (2), pp121-125.
- Sauravjoyti Sarmah and Dhruba K. Bhattacharyya. May 2010 “An Effective Technique for Clustering Incremental Gene Expression data”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 3.
- Wei Zhong, Gulsah Altun, Robert Harrison, Phang C. Tai, and Yi Pan, September 2005 “Improved K-Means Clustering Algorithm for Exploring Local Protein Sequence Motifs Representing Common Structural Property”, IEEE transactions on nanobioscience, Vol. 4, no. 3.
- Y. Lu, S. Lu, F. Fotouhi, Y. Deng, and S. Brown, 2004 “Incremental Genetic K-Means Algorithm and its Application in Gene Expression Data Analysis”, BMC Bioinformatics.
Index Terms
Keywords