Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters

Curi, María Eugenia; Carozzi, Lucía; Massobrio, Renzo; Nesmachnow, Sergio; DANOY, Grégoire; OSTASZEWSKI, Marek; BOUVRY, Pascal

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Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters

Curi, María Eugenia; Carozzi, Lucía; Massobrio, Renzo et al.

2018 • In Bioinspired Optimization Methods and Their Applications

Peer reviewed

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https://hdl.handle.net/10993/35740

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Abstract :

[en] This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the evolutionary search as simple as possible in order to scale up for solving large instances. The experimental evaluation is performed considering a set of real problem instances, including a real-life problem of analyzing biomedical information in the Parkinson's disease map project. The main results demonstrate that the proposed evolutionary approaches are able to compute accurate trade-off solutions and efficiently handle the problem instance involving biomedical information.

Disciplines :

Computer science

Author, co-author :

Curi, María Eugenia

Carozzi, Lucía

Massobrio, Renzo

Nesmachnow, Sergio

DANOY, Grégoire ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)

OSTASZEWSKI, Marek ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)

BOUVRY, Pascal ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)

External co-authors :

yes

Language :

English

Title :

Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters

Publication date :

2018

Event name :

8th International Conference on Bioinspired Optimization Methods and Their Applications (BIOMA)

Event date :

16-05-2018

Audience :

International

Main work title :

Bioinspired Optimization Methods and Their Applications

Publisher :

Springer International Publishing, Cham, Unknown/unspecified

ISBN/EAN :

978-3-319-91641-5

Peer reviewed :

Peer reviewed

Commentary :

100--112

Available on ORBilu :

since 25 May 2018

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Bibliography

Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)
Welch, W.: Algorithmic complexity: Three NP- hard problems in computational statistics. J. Stat. Comput. Simul. 15(1), 17–25 (1982)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
Nesmachnow, S.: An overview of metaheuristics: accurate and efficient methods for optimisation. Int. J. Metaheuristics 3(4), 320–347 (2014)
Hruschka, E., Campello, R., Freitas, A., de Carvalho, A.: A survey of evolutionary algorithms for clustering. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 39(2), 133–155 (2009)
Sheng, W., Liu, X.: A hybrid algorithm for k-medoid clustering of large data sets. In: IEEE Congress on Evolutionary Computation, pp. 77–82 (2004)
University of Luxembourg: Parkinson’s disease map project http://wwwen.uni.lu/lcsb/research/parkinsonsdiseasemap, November 2017
Fujita, K., et al.: Integrating pathways of Parkinson’s disease in a molecular interaction map. Mol. Neurobiol. 49(1), 88–102 (2014)
Das, S., Abraham, A., Konar, A.: Metaheuristic Clustering. Studies in Computational Intelligence, vol. 178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-93964-1
Deng, Y., Bard, J.: A reactive GRASP with path relinking for capacitated clustering. J. Heuristics 17(2), 119–152 (2011)
Cowgill, M., Harvey, R., Watson, L.: A genetic algorithm approach to cluster analysis. Comput. Mathematics Appl. 37(7), 99–108 (1999)
Bandyopadhyay, S., Maulik, U.: Nonparametric genetic clustering: comparison of validity indices. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 31(1), 120– 125 (2001)
Maulik, U., Bandyopadhyay, S., Mukhopadhyay, A.: Multiobjective Genetic Algorithms for Clustering. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-16615-0
Ripon, K., Tsang, C.H., Kwong, S., Ip, M.K.: Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm. In: 18th International Conference on Pattern Recognition, pp. 3609–3616 (2006)
Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Trans. Evol. Comput. 11(1), 56–76 (2007)
Korkmaz, E., Du, J., Alhajj, R., Barker, K.: Combining advantages of new chromosome representation scheme and multi-objective genetic algorithms for better clustering. Intell. Data Anal. 10(2), 163–182 (2006)
Deaven, D., Ho, K.: Molecular geometry optimization with a genetic algorithm. Phys. Rev. Lett. 75, 288–291 (1995)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)
Ministerio de Vivienda Ordenamiento Territorial y Medio Ambiente (Uruguay): Red de estaciones hidrométricas http://www.mvotma.gub.uy, November 2017
Alcalá, J., et al.: KEEL data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. J. Multiple-valued Logic Soft Comput. 17(2–3), 255–287 (2010)
Luke, S., et al.: ECJ 23: A Java-based Evolutionary Computation Research System. https://cs.gmu.edu/eclab/projects/ecj. Accessed March 2017
Kaufman, L., Rousseeuw, P.: Clustering by means of medoids. In: Statistical Data Analysis Based on the L1-Norm and Related Methods (1987)