Abstract
The Competitive Learning Neural Network (CLNN) algorithm is used for the classification of numerical datasets. The Adaptive Competitive Learning Neural Network (ACLNN) algorithm is a modification of the CLNN algorithm and produces high accuracy results with small and moderate sizes of datasets. However, it has high time complexity with big datasets, and the accuracy of the data clustering is low. To overcome these drawbacks, a Multi-phase Adaptive Competitive Learning Neural Network (MACLNN) is proposed. The proposed algorithm consists of three phases. The first phase in the proposed algorithm splits big datasets into equal partitions called sub-datasets. This phase aims to keep the dataset’s characteristics and speeding up the clustering process. The second phase in the proposed algorithm uses the sub-datasets as input data to the ACLNN algorithm. This phase aims to determine the optimal number of clusters. To speed up this phase, a parallel processing technique is used. The last phase in the proposed algorithm uses the extensive dataset and the optimal number of clusters determined from the second phase as input to the ACLNN algorithm. This phase aims to determine the clustering id for every data object in the input dataset. To assess the effectiveness of the proposed algorithm, twelve experimental datasets are used. The results obtained are compared to those obtained by the ACLNN algorithm. Evaluation of the proposed algorithm on big datasets shows that it outperforms the ACLNN algorithm on both the clustering accuracy and the running time.
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References
Oyedotun, O.K., Khashman, A.: Banknote recognition: investigating processing and cognition framework using competitive neural network. Cogn. Neurodyn. 11, 67–79 (2017). https://doi.org/10.1007/s11571-016-9404-2
Li, W., Gu, Y., Yin, D., Xia, T., Wang, J.: Research on the community number evolution model of public opinion based on stochastic competitive learning. IEEE Access 8, 46267–46277 (2020). https://doi.org/10.1109/ACCESS.2020.2978522
Zidan, M., Abdel-Aty, A.-H., El-shafei, M., Feraig, M., Al-Sbou, Y., Eleuch, H., Abdel-Aty, M.: Quantum classification algorithm based on competitive learning neural network and entanglement measure. Appl. Sci. 9, 1277 (2019). https://doi.org/10.3390/app9071277
Qu, L., Zhao, Z., Wang, L., Wang, Y.: Efficient and hardware-friendly methods to implement competitive learning for spiking neural networks. Neural Comput. Appl. 32, 13479–13490 (2020). https://doi.org/10.1007/s00521-020-04755-4
Li, T., Kou, G., Peng, Y., Shi, Y.: Classifying with adaptive hyper-spheres: an incremental classifier based on competitive learning. IEEE Trans. Syst. Man Cybern. Syst. 50, 1218–1229 (2020). https://doi.org/10.1109/TSMC.2017.2761360
Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network Toolbox TM User’s Guide R2017b. Mathworks Inc. (2017)
Beale, M.H., Hagan, M.T., Demuth, H.B.: Deep Learning Toolbox User’s Guide. Mathworks Inc., Herborn (2018)
Wickramasinghe, C.S., Amarasinghe, K., Manic, M.: Deep self-organizing maps for unsupervised image classification. IEEE Trans. Ind. Inform. 15, 5837–5845 (2019). https://doi.org/10.1109/TII.2019.2906083
Kohonen, T.: Self-organization and Associative Memory. Springer, Heidelberg (2012)
Fukunaga, K.: Introduction to statistical pattern recognition. Elsevier (2013)
Du, K.L., Swamy, M.N.S.: Neural networks and statistical learning, Second edn. Springer, London (2019). https://doi.org/10.1007/978-1-4471-7452-3
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (2007)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Budura, G., Botoca, C., Miclău, N.: Competitive learning algorithms for data clustering. Facta Univ. Electron. Energ. 19, 261–269 (2006)
Dinkelbach, H.Ü., Vitay, J., Beuth, F., Hamker, F.H.: Comparison of GPU-and CPU-implementations of mean-firing rate neural networks on parallel hardware. Netw. Comput. Neural Syst. 23, 212–236 (2012). https://doi.org/10.3109/0954898X.2012.739292
Li, X., Zhang, G., Li, K., Zheng, W.: Deep learning and its parallelization. In: Big Data Princ. Paradig., pp. 95–118. Elsevier Inc. (2016). https://doi.org/10.1016/B978-0-12-805394-2.00004-0
Ploskas, N., Samaras, N.: GPU Programming in MATLAB. Morgan Kaufmann (2016)
Abas, A.R.: Adaptive competitive learning neural networks. Egypt. Informatics J. 14, 183–194 (2013). https://doi.org/10.1016/j.eij.2013.08.001
Abas, A.R.: On determining efficient finite mixture models with compact and essential components for clustering data. Egypt. Informatics J. 14, 79–88 (2013). https://doi.org/10.1016/j.eij.2013.02.002
C. Mathworks, Parallel Computing Toolbox TM User’s Guide R 2018 a (2018)
Hidalgo Espinoza, S.H.: Intrusion Detection in Web Systems Using Deep Learning Techniques, Universidad de Investigación de Tecnología Experimental Yachay (2019)
Serbedzija, N.B.: Simulating artificial neural networks on parallel architectures. Comput. (Long. Beach. Calif) 29, 56–63 (1996)
Heard, M., Ford, J., Yene, N., Straiton, B., Havanas, P., Guo, L.: Advancing the neurocomputer. Neurocomputing 284, 36–51 (2018). https://doi.org/10.1016/j.neucom.2018.01.021
Chu, C.-T., Kim, S.K., Lin, Y.-A., Yu, Y., Bradski, G., Olukotun, K., Ng, Y.: Map-reduce for machine learning on multicore. In: Advances Neural Information Processing System, pp. 281–288 (2007)
Kharbanda, H., Campbell, R.H.: Fast neural network training on general purpose computers. in: Proceedings of International Conference High Performance Computing (2011)
Dua, D., Graff, C.: {UCI} Machine Learning Repository (2017). https://archive.ics.uci.edu/ml
Ilager, S., Prasad, P.S.V.S.S.: Scalable mapreduce-based fuzzy min-max neural network for pattern classification, In: ACM International Conference Proceeding Series. Association for Computing Machinery (2017). https://doi.org/10.1145/3007748.3007776
Amelio, A., Tagarelli, A.: Data mining: clustering, in: Encyclopedia Bioinformatics Computational Biology ABC Bioinformatics, pp. 437–448. Elsevier (2018). https://doi.org/10.1016/B978-0-12-809633-8.20489-5
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Mahdy, M.G., Abas, A.R., Mahmoud, T.M. (2021). Multi-phase Adaptive Competitive Learning Neural Network for Clustering Big Datasets. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_65
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