Abstract
In the present paper, we propose an iterative clustering approach that sequentially applies five processes, namely: the assign, delete, split, delete and optimization. It is based on the fitness probability scores of the cluster centers to identify the least fitted centers to undergo an optimization process, aiming to improve the centers from one iteration to another. Moreover, the parameters of the algorithm for the delete, split and optimization processes are dynamically tuned as problem dependent functions. The presented clustering algorithm is evaluated using four data sets, two randomly generated and two well-known sets. The obtained clustering algorithm is compared with other clustering algorithms through the visualization of the clustering, the value of a validity measure and the value of the objective function of the optimization process. The comparison of results shows that the proposed clustering algorithm is effective and robust.
This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00013/2020 and UIDP/00013/2020 of CMAT-UM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Le Cam, L.M., Neyman, J. (eds.) Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, vol. 1, pp. 281–297 (1967)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KKD-1996 Proceedings, pp. 226–231. AAAI Press (1996)
Mohammed, J.Z., Meira, Jr., W.: Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd (edn.). Cambridge University Press, Cambridge (2020)
Mirkin, B.: Clustering For Data Mining: A Data Recovery Approach. Computer Science and Data Analysis Series. Chapman & Hall/CRC, Boca Raton (2005)
Higham, D.J., Kalna, G., Kibble, M.: Spectral clustering and its use in bioinformatics. J. Comput. Appl. Math. 204, 25–37 (2007)
Haraty, R.A., Dimishkich, M., Masud, M.: An enhanced k-means clustering algorithm for pattern discovery in healthcare data. Int. J. Distrib. Sens. Netw. 2015, Article ID 615740, p. 11 (2015)
Sarkar, M., Yegnanarayana, B., Khemani, D.: A clustering algorithm using evolutionary programming-based approach. Pattern Recogn. Lett. 18, 975–986 (1997)
Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evaluation algorithm. IEEE Trans. Syst. Man Cyber. Syst. 38, 218–237 (2008)
Kwedlo, W.: A clustering method combining differential evolution with K-means algorithm. Pattern Recogn. Lett. 32, 1613–1621 (2011)
Cura, T.: A particle swarm optimization approach to clustering. Expert Syst. Appl. 39, 1582–1588 (2012)
Patel, K.G.K., Dabhi, V.K., Prajapati, H.B.: Clustering using a combination of particle swarm optimization and K-means. J. Intell. Syst. 26(3), 457–469 (2017)
El-Shorbagy, M.A., Ayoub, A.Y., Mousa, A.A., El-Desoky, I.M.: An enhanced genetic algorithm with new mutation for cluster analysis. Comput. Stat. 34, 1355–1392 (2019)
Ezugwu, A.E.-S., Agbaje, M.B., Aljojo, N., Els, R., Chiroma, H., Elaziz, M.A.: A comparative performance of hybrid firefly algorithms for automatic data clustering. IEEE Access 8, 121089–121118 (2020)
He, Z., Yu, C.: Clustering stability-based evolutionary K-means. Soft. Comput. 23, 305–321 (2019)
Memarsadeghi, N., Mount, D.M., Netanyahu, N.S., Le Moigne, J.: A fast implementation of the ISODATA clustering algorithm. Int. J. Computat. Geom. Appl. 17(1), 71–103 (2007)
Prabha, K.A., Visalakshi, N.K.: Improved particle swarm optimization based K-means clustering. In: International Conference on Intelligent Computing Applications, pp. 59–63. IEEE Publisher CPS (2014). https://doi.org/10.1109/ICICA.2014.21
Heris, M.K.: Evolutionary Data Clustering in MATLAB. https://yarpiz.com/64/ypml101-evolutionary-clustering Yarpiz (2015)
Asvadi, A.: K-means Clustering Code. Department of ECE, SPR Lab., Babol (Noshirvani) University of Technology (2013). http://www.a-asvadi.ir/
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1(2), 224–227 (1979)
Dua, D., Graff, C.: UCI machine learning repository http://archive.ics.uci.edu/ml. University of California, School of Information and Computer Science, Irvine (2019)
Acknowledgments
The authors wish to thank two anonymous referees for their comments and suggestions to improve the paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Costa, M.F.P., Rocha, A.M.A.C., Fernandes, E.M.G.P. (2021). A Clustering Algorithm Based on Fitness Probability Scores for Cluster Centers Optimization. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12953. Springer, Cham. https://doi.org/10.1007/978-3-030-86976-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-86976-2_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86975-5
Online ISBN: 978-3-030-86976-2
eBook Packages: Computer ScienceComputer Science (R0)