Abstract
K-means algorithm is one of the most widely used methods in data mining and statistical data analysis to partition several objects in K distinct groups, called clusters, on the basis of their similarities. The main problel and distributed clustering algorithms start to be designem of this algorithm is that it requires the number of clusters as an input data, but in the real life it is very difficult to fix in advance such value. In this work we propose a parallel modified K-means algorithm where the number of clusters is increased at run time in a iterative procedure until a given cluster quality metric is satisfied. To improve the performance of the procedure, at each iteration two new clusters are created, splitting only the cluster with the worst value of the quality metric. Furthermore, experiments in a multi-core CPUs based environment are presented.
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References
Abubaker, M., Ashour, W.M.: Efficient data clustering algorithms: improvements over K-means. Int. J. Intell. Syst. Appl. 5, 37–49 (2013)
Aggarwal, C.C., Reddy, C.K.: Data Clustering, Algorithms and Applications. Chapman and Hall/CRC, London (2013)
Andrade, G., Ramos, G., Madeira, D., Sachetto, R., Ferreira, R., Rocha, L.: G-DBSCAN: a GPU accelerated algorithm for density-based clustering. Procedia Comput. Sci. 18, 369–378 (2013)
Boccia, V., Carracciuolo, L., Laccetti, G., Lapegna, M., Mele, V.: HADAB: enabling fault tolerance in parallel applications running in distributed environments. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2011. LNCS, vol. 7203, pp. 700–709. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31464-3_71
Caruso, P., Laccetti, G., Lapegna, M.: A performance contract system in a grid enabling, component based programming environment. In: Sloot, P.M.A., Hoekstra, A.G., Priol, T., Reinefeld, A., Bubak, M. (eds.) EGC 2005. LNCS, vol. 3470, pp. 982–992. Springer, Heidelberg (2005). https://doi.org/10.1007/11508380_100
D’Ambra, P., Danelutto, M., di Serafino, D., Lapegna, M.: Advanced environments for parallel and distributed applications: a view of the current status. Parallel Comput. 28, 1637–1662 (2002)
D’Ambra, P., Danelutto, M., di Serafino, D., Lapegna, M.: Integrating MPI-based numerical software into an advanced parallel computing environment. In: Proceedings of the Eleventh Euromicro Conference on Parallel Distributed and Network-based Procesing, Clematis ed., pp. 283–291. IEEE (2003)
D’Apuzzo, M., Lapegna, M., Murli, A.: Scalability and load balancing in adaptive algorithms for multidimensional integration. Parallel Comput. 23, 1199–1210 (1997)
Di Fatta, G., Blasa, F., Cafiero, S., Fortino, G.: Fault tolerant decentralised K-means clustering for asynchronous large-scale networks. J. Parallel Distrib. Comput. 73(2013), 317–329 (2013)
Dua, D., Graff, C.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2017). http://archive.ics.uci.edu/ml
Frey, P.W., Slate, D.J.: Letter recognition using Holland-style adaptive classifiers. Mach. Learn. 6, 161–182 (1991)
Gan, D.G., Ma, C., Wu, J.: Data Clustering: Theory, Algorithms, and Applications. ASA-SIAM Series on Statistics and Applied Probability. SIAM, Philadelphia. ASA, Alexandria (2007)
Gregoretti, F., Laccetti, G., Murli, A., Oliva, G., Scafuri, U.: MGF: a grid-enabled MPI library. Future Gener. Comput. Syst. 24, 158–165 (2008)
He, Y., Tan, H., Luo, W., Feng, S., Fan, J.: MR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed data. Front. Comput. Sci. 8, 83–99 (2014)
Huang, Z.X.: Extensions to the K-means algorithm for clustering large datasets with categorical values. Data Min. Knowl. Disc. 2, 283–304 (1998)
Joshi, A., Kaur, R.: A review: comparative study of various clustering techniques in data mining. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3, 55–57 (2013)
Karypis, G., Kumar, V.: Parallel multilevel K-way partitioning for irregular graphs. SIAM Rev. 41, 278–300 (1999)
Laccetti, G., Lapegna, M.: PAMIHR. a parallel FORTRAN program for multidimensional quadrature on distributed memory architectures. In: Amestoy, P., et al. (eds.) Euro-Par 1999. LNCS, vol. 1685, pp. 1144–1148. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48311-X_160
Laccetti, G., Lapegna, M., Mele, V., Montella, R.: An adaptive algorithm for high-dimensional integrals on heterogeneous CPUGPU systems. Concurr. Comput. Pract. Exp. 31, e4945 (2018)
Laccetti, G., Lapegna, M., Mele, V., Romano, D., Murli, A.: A double adaptive algorithm for multidimensional integration on multicore based HPC systems. Int. J. Parallel Program. 40, 397–409 (2012)
Laccetti, G., Lapegna, M., Mele, V., Romano, D.: A study on adaptive algorithms for numerical quadrature on heterogeneous GPU and multicore based systems. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2013. LNCS, vol. 8384, pp. 704–713. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-55224-3_66
Laccetti, G., Lapegna, M., Mele, V.: A loosely coordinated model for heap-based priority queues in multicore environments. Int. J. Parallel Prog. 44, 901–921 (2016)
Lapegna, M.: A global adaptive quadrature for the approximate computation of multidimensional integrals on a distributed memory multiprocessor. Concurr. Pract. Exp. 4, 413–426 (1992)
Patibandla, R.S.M.L., Veeranjaneyulu, N.: Survey on clustering algorithms for unstructured data. In: Bhateja, V., Coello Coello, C.A., Satapathy, S.C., Pattnaik, P.K. (eds.) Intelligent Engineering Informatics. AISC, vol. 695, pp. 421–429. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7566-7_41
Pelleg, D., Moore, A.W.: X-means: extending k-means with efficient estimation of the number of clusters. In: Proceedings of the 17th International Conference on Machine Learning, pp. 727–734. Morgan Kaufmann (2000)
Pena, J.M., Lozano, J.A., Larranaga, P.: An empirical comparison of four initialization methods for the K-means algorithm. Pattern Recogn. Lett. 20, 1027–1040 (1999)
Shindler, M., Wong, A., Meyerson, A.: Fast and accurate k-means for large datasets. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F.C.N., Weinberger, K.Q. (eds.): Proceedings of 25th Annual Conference on Neural Information Processing Systems, pp. 2375–2383 (2011)
Shirkhorshidi, A.S., Aghabozorgi, S., Wah, T.Y., Herawan, T.: Big data clustering: a review. In: Murgante, B., et al. (eds.) ICCSA 2014. LNCS, vol. 8583, pp. 707–720. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09156-3_49
Xu, D., Tian, Y.: A comprehensive survey of clustering algorithms. Ann. Data Sci. 2, 165–193 (2015)
Xu, R., Wunsch, D.: Survey of clustering algorithms. Trans. Neural Netw. 16, 645–678 (2005)
Zhao, W., Ma, H., He, Q.: Parallel K-means clustering based on MapReduce. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) CloudCom 2009. LNCS, vol. 5931, pp. 674–679. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10665-1_71
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Laccetti, G., Lapegna, M., Mele, V., Romano, D. (2019). A High Performance Modified K-Means Algorithm for Dynamic Data Clustering in Multi-core CPUs Based Environments. In: Montella, R., Ciaramella, A., Fortino, G., Guerrieri, A., Liotta, A. (eds) Internet and Distributed Computing Systems . IDCS 2019. Lecture Notes in Computer Science(), vol 11874. Springer, Cham. https://doi.org/10.1007/978-3-030-34914-1_9
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