Top

Published in:

2018 | OriginalPaper | Chapter

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

Authors : María Eugenia Curi, Lucía Carozzi, Renzo Massobrio, Sergio Nesmachnow, Grégoire Danoy, Marek Ostaszewski, Pascal Bouvry

Published in: Bioinspired Optimization Methods and Their Applications

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Constructive Metaheuristics for the Set Covering Problem

next chapter Evolutionary Algorithms for Scheduling of Crude Oil Preheating Process Under Linear Fouling

Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)CrossRef

Welch, W.: Algorithmic complexity: Three NP- hard problems in computational statistics. J. Stat. Comput. Simul. 15(1), 17–25 (1982)MathSciNetCrossRef

Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7CrossRefMATH

Nesmachnow, S.: An overview of metaheuristics: accurate and efficient methods for optimisation. Int. J. Metaheuristics 3(4), 320–347 (2014)CrossRef

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)CrossRef

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/parkinson_s_disease_map, November 2017

Fujita, K., et al.: Integrating pathways of Parkinson’s disease in a molecular interaction map. Mol. Neurobiol. 49(1), 88–102 (2014)CrossRef

Das, S., Abraham, A., Konar, A.: Metaheuristic Clustering. Studies in Computational Intelligence, vol. 178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-93964-1CrossRefMATH

10.

Deng, Y., Bard, J.: A reactive GRASP with path relinking for capacitated clustering. J. Heuristics 17(2), 119–152 (2011)CrossRef

11.

Cowgill, M., Harvey, R., Watson, L.: A genetic algorithm approach to cluster analysis. Comput. Mathematics Appl. 37(7), 99–108 (1999)MathSciNetCrossRef

12.

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)CrossRef

13.

Maulik, U., Bandyopadhyay, S., Mukhopadhyay, A.: Multiobjective Genetic Algorithms for Clustering. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-16615-0CrossRefMATH

14.

Ripon, K., Tsang, C.H., Kwong, S., Ip, M.K.: Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm. In: 18\(^{th}\) International Conference on Pattern Recognition, pp. 3609–3616 (2006)

15.

Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Trans. Evol. Comput. 11(1), 56–76 (2007)CrossRef

16.

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)

17.

Deaven, D., Ho, K.: Molecular geometry optimization with a genetic algorithm. Phys. Rev. Lett. 75, 288–291 (1995)CrossRef

18.

Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)MATH

19.

Ministerio de Vivienda Ordenamiento Territorial y Medio Ambiente (Uruguay): Red de estaciones hidrométricas http://www.mvotma.gub.uy, November 2017

20.

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)

21.

Luke, S., et al.: ECJ 23: A Java-based Evolutionary Computation Research System. https://cs.gmu.edu/eclab/projects/ecj. Accessed March 2017

22.

Kaufman, L., Rousseeuw, P.: Clustering by means of medoids. In: Statistical Data Analysis Based on the L1-Norm and Related Methods (1987)

Title: Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters
Authors: María Eugenia Curi
Lucía Carozzi
Renzo Massobrio
Sergio Nesmachnow
Grégoire Danoy
Marek Ostaszewski
Pascal Bouvry
Publisher: Springer International Publishing
Book: Bioinspired Optimization Methods and Their Applications
Print ISBN: 978-3-319-91640-8

Electronic ISBN: 978-3-319-91641-5

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-91641-5_9

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner