Abstract
Clustering is one of the main data mining tasks. It can be performed on a fuzzy or a crisp basis. Fuzzy clustering is widely-applied with microarray gene expression data as these data are usually uncertain and imprecise. There are several measures to evaluate the quality of clustering, but their performance is highly related to the dataset to which they are applied. In a previous work the authors proposed using a multi-objective genetic algorithm – based method, NSGA – II, to optimize two clustering validity measures simultaneously. In this paper we use another multi-objective optimizer, NSPSO, which is based on the particle swarm optimization algorithm, to solve the same problem. The experiments we conducted on two microarray gene expression data show that NSPSO is superior to NSGA-II in handling this problem.
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References
El-Ghazali, T.: Metaheuristics: from design to implementation. Wiley (2009)
Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S.: Multiobjective Evolutionary Approach to Fuzzy Clustering of Microarray Data, pp. 303–328. World Scientific, Singapore (2007)
Muhammad Fuad, M.M.: Differential evolution versus genetic algorithms: towards symbolic aggregate approximation of non-normalized time series. In: Sixteenth International Database Engineering & Applications Symposium– IDEAS’12, Prague, Czech Republic, Published by BytePress/ACM, 8–10 Aug 2012
Muhammad Fuad, M.M.: Using differential evolution to set weights to segments with different information content in the piecewise aggregate approximation. In: 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2012, San Sebastian, Spain, Published in Frontiers of Artificial Intelligence and Applications (FAIA), IOS Press, 10–12 Sept 2012
Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann (2011)
Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley, New York (2005)
Kanungo, T., Netanyahu, N.S., Wu, A.Y.: An Efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7) (2002)
Krzysztof, J.C., Pedrycz, W., Swiniarski, R.W., Kurgan, L.A.: Data Mining: A Knowledge Discovery Approach. Springer-Verlag New York, Inc., Secaucus (2007)
Maulik, U., Bandyopadhyay, S., Mukhopadhyay, A.: Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics. Springer (2011)
Gorunescu, F.: Data mining: Concepts. Blue Publishing House, Cluj-Napoca, Models (2006)
Muhammad Fuad, M.M.: Differential evolution-based weighted combination of distance metrics for k-means clustering. In: The 3rd International Conference on the Theory and Practice of Natural Computing – TPNC 2014, Granada, Spain. Published in Lecture Notes in Computer Science, vol. 8890, 9–11 Dec 2014
Muhammad Fuad, M.M.: On the application of bio-inspired optimization algorithms to fuzzy c-means clustering of time series. In: The 4th International Conference on Pattern Recognition Applications and Methods - ICPRAM 2015, Lisbon, Portugal. SCITEPRESS Digital Library, 10–12 Jan 2015
Lodhi, H., Muggleton, S.: Elements of Computational Systems Biology. Wiley, New York (2010)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. (2002)
Ma, Q., Xu, D., Iv, P., Shi, Y.: Application of NSGA-II in Parameter Optimization of Extended State Observer. Challenges of Power Engineering and Environment (2007)
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms with CD-ROM. Wiley-Interscience, (2004)
Li, X.: A Non-dominated Sorting particle swarm optimizer for multi-objective optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2003), Lecture Notes in Computer Science, vol. 2723, pp. 37–48. Springer (2003)
Reyes Sierra, M., Coello Coello, C.: Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int. J. Comput. Intell. Res. 2(3), 287–308 (2006)
Maulik, U., Mukhopadhyay, A., and Bandyopadhyay, S.: Combining pareto-optimal clusters using supervised learning for identifying co-expressed Genes. BMC Bioinfor. 10(27), 1–16, 20 Jan 2009
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13, 841–847 (1991)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comp. Appl. Math. 20, 53–65 (1987)
Petrovic, S.: A comparison between the silhouette index and the davies-bouldin index in labelling IDS clusters. In: Proceedings of the 11th Nordic Workshop of Secure IT Systems (2006)
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Muhammad Fuad, M.M. (2015). Multi-objective Optimization for Clustering Microarray Gene Expression Data - A Comparative Study. In: Jezic, G., Howlett, R., Jain, L. (eds) Agent and Multi-Agent Systems: Technologies and Applications. Smart Innovation, Systems and Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-319-19728-9_10
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DOI: https://doi.org/10.1007/978-3-319-19728-9_10
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