Abstract
This paper illustrates a comparative study of Efficient Fitness Function and Rand Index Fitness Function, to show how Efficient Fitness Function can give better results when used to cluster gene expression data. Variance which is the main limitation of Rand Index can be improved with Efficient Fitness Function. The results are evaluated by finding the precision value (i.e. sensitivity and specificity) of the dataset. Genetic Weighted K-Mean Algorithm (GWKMA) which is used here is a hybridization of Weighted K-Mean Algorithm (WKMA) and Genetic Algorithm. WKMA is used to perform optimal partition of data. Genetic Algorithm is then applied to get the best fit gene from clusters through the fitness function, on which genetic operators like selection, crossover and mutation are performed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Hartigan, J.: Clustering Algorithms. Wiley, New York (1975)
Obitko, M.: Introduction to Genetic Algorithms (1998)
Krishna, K.K., Murty, M.M.: Genetic K-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernetics 29, 1083-4419(99)00770-0
Wehrens, R., Buydens, M.C., Fraley, C., Raftery, A.C.: Model-Based Clustering for Image Segmentation and Large Datasets Via Sampling. Journal Of Classification 21, doi:10.1007/s00357-004-001-8
Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recognition 33, 1455–1456 (2000)
Wu, F.X., Zhang, W.Z., Kusalik, A.J.: A genetic k-means clustering algorithm applied to gene expression data. In: Proceedings of The Sixteenth Canadian Conference on Artificial Intelligence, Halifax, Canada, pp. 520–526 (June 2003)
Kerdprasop, K., Kerdprasop, N., Sattayatham, P.: Weighted K-Means for Density-Biased Clustering
Tho, D.X.: Genetic Algorithms and Application in Examination Scheduling. In: Scholarly Research Paper (2009), doi:10.3239/9783640636723
Srivastava, P.R., Kim, T.H.: Application of Genetic Algorithm in Software Testing. IJSE (2009)
Dudoit, S., Fridlyland, J.: A prediction-based resampling method for estimating the number of clustering in a dataset. BMC Genome Biology 3, research 0036.1- 0036.2 (2002)
Santos, J.M., Embrechts, M.: On the use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification
Jeevanand, E.S., Abdul-Sathar, E.I.: Estimation of residual entropy function for exponential distribution from censored samples. ProbStat Forum (2009) ISSN 0974-3235
Sherlock, G., Boussard, T.H., Kasarskis, A., Binkley, G., Matese, J.C., Dwight, S.S., Kaloper, M., Weng, S., Jin, H., Ball, C.A., Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D., Cherry, J.M.: The Stanford Microarray Database. Nucleic Acids Research 29, 152–155 (2001)
Jiang, D., Tang, C., Zhang, A.: Cluster Analysis for Gene Expression Data: A Survey. IEEE Transactions on Knowledge and Data Engineering 16(11)
Yeung, K.Y., Fraley, C., Murua, A., Raftery, A.E., Ruzzo, W.L.: Model-based clustering and data transformations for gene expression data. Bioinformatics (2001)
Belacel, N., Wang, Q., Cuperlovic-Culf, M.: Clustering Methods for Microarray Gene Expression Data. OMICS 10(4) (2006)
Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering Gene Expression Patterns. Journal of Computational Biology 6, 281–297
Suresh, R.M., Dinakaran, K., Valarmathie, P.: Model based modified k-means clustering for microarray data. In: International Conference on Information Management and Engineering, vol. 13, pp. 271–273. IEEE (2009)
Sarmah, S., Bhattacharyya, D.K.: An Effective Technique for Clustering Incremental Gene Expression data. IJCSI International Journal of Computer Science Issues 7(3(3)) (2010) ISSN (Online): 1694-0784
Beşdok, E.: 3D Vision by Using Calibration Pattern with Inertial Sensor and RBF Neural Networks Sensors, vol. 9, pp. 4572–4585 (2009), doi: 10.3390/s90604572
Deshmukh, M.K., Moorthy, C.B.: Application Of Genetic Algorithm To Neural Network Model For Estimation Of Wind Power Potential. Journal of Engineering, Science and Management Education 2, 42–48 (2010)
Awad, M.: Optimization RBFNNs Parameters Using Genetic Algorithms: Applied on Function Approximation. International Journal of Computer Science and Security (IJCSS) 4(3)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Patheja, P.S., Waoo, A.A., Sharma, R. (2012). Comparison of Efficient and Rand Index Fitness Function for Clustering Gene Expression Data. In: Meghanathan, N., Chaki, N., Nagamalai, D. (eds) Advances in Computer Science and Information Technology. Computer Science and Information Technology. CCSIT 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27317-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-27317-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27316-2
Online ISBN: 978-3-642-27317-9
eBook Packages: Computer ScienceComputer Science (R0)