Abstract
Proposed paper presents a new model-based Gaussian clustering method and defines new optimization criteria for model-based clustering, which are used as fitness functions in genetic algorithm. These optimization criteria are based on different properties of covariance matrices. The proposed model-based Gaussian clustering method is compared with the well-known K-Means method that is solved by genetic algorithm or by Particle Swarm Optimization method. Our method achieves higher similarity between real classification and computed clustering results on all six presented real-world datasets. Because of the high computational requirements of the used methods we have focused on their parallelization. Due to the chosen parallel computer architecture we have combined both MPI and OpenMP programing interfaces. We show that parallelization of the proposed method is very effective and scalable on many execution units.
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References
Andrews, J.L., Mcnicholas, P.D.: Using evolutionary algorithms for model-based clustering. Pattern Recogn. Lett. 34(9), 987–992 (2013)
Banfield, J.D., Raftery, A.E.: Model-based Gaussian and non-Gaussian clustering. Biometrics 49(3), 803–821 (1993)
Chen, C.Y., Ye, F.: Particle swarm optimization algorithm and its application to clustering analysis. In: 2004 IEEE International Conference on Networking, Sensing and Control, vol. 2, pp. 789–794 (2004)
Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78(383), 553–569 (1983)
Fahad, A., et al.: A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Trans. Emerg. Top. Comput. 2(3), 267–279 (2014)
Feigelson, E., Babu, G.: Modern Statistical Methods for Astronomy: With R Applications. Cambridge University Press, Cambridge (2012)
Fong, S., Deb, S., Yang, X.S., Zhuang, Y.: Towards enhancement of performance of k-means clustering using nature-inspired optimization algorithms. Sci. World J. 2014, 16 p. (2014). https://doi.org/10.1155/2014/564829. Article ID 564829
Grama, A.: Introduction to Parallel Computing. Pearson Education. Addison-Wesley (2003)
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, November 1995
Khoshnevisan, B., et al.: A clustering model based on an evolutionary algorithm for better energy use in crop production. Stochast. Environ. Res. Risk Assess. 29(8), 1921–1935 (2015)
Koestler, D.C., Houseman, E.A.: Model-based clustering of DNA methylation array data, pp. 91–123. Springer, Dordrecht (2015)
Lamoš, F., Potocký, R.: Pravdepodobnosť a matematická štatistika: Štatistické analýzy. Alfa (1989)
Message Passing Interface Forum: A message-passing interface standard version 2.1 (2008)
OpenMP Architecture Review Board: OpenMP application program interface version 3.1 (2011)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)
Raposo, C., et al.: Automatic clustering using a genetic algorithm with new solution encoding and operators. In: Computational Science and Its Applications – ICCSA 2014, Proceedings, Part II, Cham, pp. 92–103 (2014)
Schildt, H.: The Annotated ANSI C Standard American National Standard for Programming Languages–C: ANSI/ISO 9899–1990. Osborne/McGraw-Hill, Berkeley (1990)
Scrucca, L.: Genetic algorithms for subset selection in model-based clustering, pp. 55–70. Springer, Cham (2016)
Si, M., et al.: MT-MPI: multithreaded MPI for many-core environments. In: Proceedings of the 28th ACM International Conference on Supercomputing, ICS 2014, pp. 125–134. ACM, New York (2014)
Suthaharan, S., et al.: Labelled data collection for anomaly detection in wireless sensor networks. In: 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 269–274, December 2010
Vanbinst, K., Ceulemans, E., Ghesquière, P., Smedt, B.D.: Profiles of children’s arithmetic fact development: a model-based clustering approach. J. Exp. Child Psychol. 133, 29–46 (2015)
von Borries, G., Wang, H.: Partition clustering of high dimensional low sample size data based on values. Comput. Stat. Data Anal. 53(12), 3987–3998 (2009)
Whitley, D.: A genetic algorithm tutorial. Stat. Comput. 4(2), 65–85 (1994)
Acknowledgment
We would like to thank Lukáš Csóka for his assistance with programing the method in the C programming language during his studies at the Faculty of Informatics and Information Technologies of the Slovak University of Technology in Bratislava. We would also like to thank Radoslav Harman for his supervising while this method was being created.
This work was partially supported by the Scientific Grant Agency of The Slovak Republic, Grant No. VG 1/0458/18 and APVV-16-0484.
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Laurinec, P., Jarábek, T., Lucká, M. (2019). Application of Parallel Genetic Algorithm for Model-Based Gaussian Cluster Analysis. In: Abraham, A., Gandhi, N., Pant, M. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2018. Advances in Intelligent Systems and Computing, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-16681-6_14
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DOI: https://doi.org/10.1007/978-3-030-16681-6_14
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