Standard Weight and Distribution Function Using Glowworm Swarm Optimization for Gene Expression Data
This work shows an examination of swarm insight based grouping calculations to manage the quality articulation information successfully. In this work, a quality bunching strategies have been proposed to improve the looking and the grouping execution in genomic information. Also, through execution probes genuine informational collections, the proposed strategy Fuzzy Possibilistic C-Means Algorithm utilizing Expectation Maximization Algorithm is appeared to accomplish higher productivity, bunching quality and mechanization than other grouping technique.
To keep up bond between the areas in territory, Locality Sensitive Discriminant Analysis is utilized and a productive meta experimental advancement calculation named Modified Artificial Bee Colony utilizing Fuzzy C Means grouping known as MoABC for bunching the quality articulation dependent on the example. At that point effective Standard Weight and Distribution Function - Glowworm Swarm Optimization (SWDF-GSO) grouping is utilized for bunching the quality articulation dependent scheduled on example. The trial results demonstrates that proposed calculation accomplish a higher grouping exactness and proceeds slighter fewer bunching period once contrasted and standing calculations.
KeywordsClustering LSDA MABC Fuzzy C Means Swarm intelligence
- 1.Huber, W., von Heydebreck, A., Vingron, M.: Analysis of microarray gene expression data. Max-Planck-Institute for Molecular Genetics, Berlin, 2 April 2003Google Scholar
- 2.Deng, J., Hu, J.L., Chi, H., Wu, J.: An improved fuzzy clustering method for text mining. In: Second International Conference on Networks Security Wireless Communications and Trusted Computing (NSWCTC), vol. 1, pp. 65–69 (2010)Google Scholar
- 4.Shanthi, R., Suganya, R.: Enhancement of fuzzy possibilistic C-means algorithm using EM algorithm (EMFPCM). Int. J. Comput. Appl. 61(12), 10–15 (2013). 0975–8887Google Scholar
- 5.Wang, G., Cui, W., Shao, Y.: Discriminant locality preserving projection. Res. J. Appl. Sci. Eng. Technol. 4(24), 5572–5577 (2012)Google Scholar
- 6.Holland, S.M.: Principal components analysis (PCA). Department of Geology, University of Georgia, Athens, GA (2008)Google Scholar
- 8.Khushaba, R.N., Al-Jumaily, A., Al-Ani, A.: Dimensionality reduction with neuro-fuzzy discriminant analysis. Int. J. Comput. Intell. 5, 225–232 (2009)Google Scholar
- 9.Sathishkumar, K., Narendran, P.: An efficient artificial bee colony and fuzzy C means based co-regulated biclustering from gene expression data. In: Mining Intelligence and Knowledge Exploration, LNCS, vol. 8284, pp. 120–129. Springer (2013)Google Scholar
- 10.Sathishkumar, K., Ramalingam, M., Thiagarasu, V.: Biclustering of gene expression using glowworm swarm optimization and neuro-fuzzy discriminant analysis. Int. J. Adv. Res. Comput. Sci. Softw. Eng (IJARCSE) 4(1), 188–196 (2014). ISSN 2277 128XGoogle Scholar