Open Access

Shared Subscribe Hyper Simulation Optimization (SUBHSO) Algorithm for Clustering Big Data – Using Big Databases of Iran Electricity Market


Cite

[1] H. Chen, and Z. Mao, “Study on the failure probability of occupant evacuation with the method of Monte Carlo sampling,” Procedia Engineering, vol. 211, 2018, pp. 55–62. https://doi.org/10.1016/j.proeng.2017.12.13710.1016/j.proeng.2017.12.137Search in Google Scholar

[2] T. G. Penkova, “Principal component analysis and cluster analysis for evaluating the natural andanthropogenic territory safety,” Procedia Computer Science, vol. 112, 2017, pp. 99–108. https://doi.org/10.1016/j.procs.2017.08.17910.1016/j.procs.2017.08.179Search in Google Scholar

[3] E. Vera, D. Lucio, L. A. F. Fernandes, and L. Velho, “Hough transform for real-time plane detection in depth images,” Pattern Recognition Letters, vol. 103, 2018, pp. 8–15. https://doi.org/10.1016/j.patrec.2017.12.02710.1016/j.patrec.2017.12.027Search in Google Scholar

[4] M. H. Yang, J. H. Li, and B. X. Liu, “Fractal analysis on the cluster network in metallic liquid and glass,” Journal of Alloys and Compounds, vol. 757, 2018, pp. 228–232. https://doi.org/10.1016/j.jallcom.2018.05.06910.1016/j.jallcom.2018.05.069Search in Google Scholar

[5] T. Cui, F. Caravelli, and C. Ududec, “Correlations and clustering in wholesale electricity markets,” Physica A: Statistical Mechanics and its Applications, vol. 492, 2018, pp. 1507–1522. https://doi.org/10.1016/j.physa.2017.11.07710.1016/j.physa.2017.11.077Search in Google Scholar

[6] G. Zhu, J. Wang, and H. Lu, “Clustering based ensemble correlation tracking,” Computer Vision and Image Understanding, vol. 153, 2016, pp. 55–63. https://doi.org/10.1016/j.cviu.2016.05.00610.1016/j.cviu.2016.05.006Search in Google Scholar

[7] S. Chormunge, and S. Jena, “Correlation based feature selection with clustering for high dimensional data,” Journal of Electrical Systems and Information Technology, vol. 5, no. 3, 2018, pp. 542–549. https://doi.org/10.1016/j.jesit.2017.06.00410.1016/j.jesit.2017.06.004Search in Google Scholar

[8] K. Fujiwara, M. Kano, and S. Hasebe, “Development of correlation-based clustering method and its application to software sensing,” Chemometrics and Intelligent Laboratory Systems, vol. 101, no. 2, 2010, pp. 130–138. https://doi.org/10.1016/j.chemolab.2010.02.00610.1016/j.chemolab.2010.02.006Search in Google Scholar

[9] R. Veroneze, A. Banerjee, and F. J. von Zuben, “Enumerating all maximal biclusters in numerical datasets,” Information Sciences, vol. 379, 2017, pp. 288–309. https://doi.org/10.1016/j.ins.2016.10.02910.1016/j.ins.2016.10.029Search in Google Scholar

[10] S. Chen, J. Liu, and T. Zeng, “Measuring the quality of linear patterns inbiclusters,” Methods, vol. 83, 2015, pp. 18–27. https://doi.org/10.1016/j.ymeth.2015.04.00510.1016/j.ymeth.2015.04.00525890245Search in Google Scholar

[11] G. F. de Sousa Filho, L. dos A. F. Cabral, L. S. Ochi, and F. Protti, “Hybrid metaheuristic for bicluster editing problem,” Electronic Notes in Discrete Mathematics, vol. 39, 2012, pp. 35–42. https://doi.org/10.1016/j.endm.2012.10.00610.1016/j.endm.2012.10.006Search in Google Scholar

[12] M. Wang, X. Shang, X. Li, W. Liu, and Z. Li, “Efficient mining differential co-expression biclusters in microarray datasets,” Gene, vol. 518, no. 1, 2013, pp. 59–69. https://doi.org/10.1016/j.gene.2012.11.08510.1016/j.gene.2012.11.08523276708Search in Google Scholar

[13] Y. Lee, J. Lee, and C. H. Jun, “Stability-based validation of bicluster solutions,” Pattern Recognition, vol. 44, no. 2, 2011, pp. 252–264. https://doi.org/10.1016/j.patcog.2010.08.02910.1016/j.patcog.2010.08.029Search in Google Scholar

[14] F. Divina, B. Pontes, R. Giráldez, and J. S. Aguilar-Ruiz, “An effective measure for assessing the quality of biclusters,” Computers in Biology and Medicine, vol. 42, no. 2, 2012, pp. 245–256. https://doi.org/10.1016/j.compbiomed.2011.11.01510.1016/j.compbiomed.2011.11.01522196882Search in Google Scholar

[15] C. C. Aggarwal, J. L. Wolf, P. S. Yu, C. Procopiuc, and J. S. Park, “Fast algorithms for projected clustering,” Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, SIGMOD, ACM, New York, NY, USA, 1999, pp. 61–72. https://doi.org/10.1145/304181.30418810.1145/304181.304188Search in Google Scholar

[16] G. Moise, J. Sander, and M. Ester, “Robust projected clustering,” Knowledge and Information Systems, vol. 14, no. 3, 2008, pp. 273–298. https://doi.org/10.1007/s10115-007-0090-610.1007/s10115-007-0090-6Search in Google Scholar

[17] G. Gan, and J. Wu, “A convergence theorem for the fuzzy subspace clustering (fsc) algorithm,” Pattern Recognition, vol. 6, no. 2, 2008, pp. 1939–1947. https://doi.org/10.1016/j.patcog.2007.11.01110.1016/j.patcog.2007.11.011Search in Google Scholar

[18] Z. Deng, K. S. Choi, F. L. Chung, and S. Wang, “Enhanced soft subspace clustering integrating within-cluster and between-cluster information,” Pattern Recognition, vol. 43, no. 3, 2010, pp. 767–781. https://doi.org/10.1016/j.patcog.2009.09.01010.1016/j.patcog.2009.09.010Search in Google Scholar

[19] X. Chen, Y. Ye, X. Xu, and J. Z. Huang, “A feature group weighting method for subspace clustering of high-dimensional data,” Pattern Recognition, vol. 45, no. 1, 2012, pp. 434–446. https://doi.org/10.1016/j.patcog.2011.06.00410.1016/j.patcog.2011.06.004Search in Google Scholar

[20] D. S. Modha, and W. S. Spangler, “Feature weighting in k-means clustering,” Machine Learning, vol. 52, no. 3, 2003, pp. 217–237. https://doi.org/10.1023/A:102401660952810.1023/A:1024016609528Search in Google Scholar

[21] C. Domeniconi, D. Gunopulos, S. Ma, B. Yan, M. Al-Razgan, and D. Papadopoulos, “Locally adaptive metrics for clustering high dimensional data,” Data Mining and Knowledge Discovery, vol. 14, no. 1, 2007, pp. 63–97. https://doi.org/10.1007/s10618-006-0060-810.1007/s10618-006-0060-8Search in Google Scholar

[22] Y. Zhu, K. M. Ting, and M. J. Carman, “Grouping points by shared subspaces for effective subspace clustering,” Pattern Recognition, vol. 83, 2018, pp. 230–244. https://doi.org/10.1016/j.patcog.2018.05.02710.1016/j.patcog.2018.05.027Search in Google Scholar

[23] H. Chen, W. Wang, and X. Feng, “Structured sparse subspace clustering with within-cluster grouping,” Pattern Recognition, vol. 83, 2018, pp. 107–118. https://doi.org/10.1016/j.patcog.2018.05.02010.1016/j.patcog.2018.05.020Search in Google Scholar

[24] W. Zhu, J. Lu, and J. Zhou, “Nonlinear subspace clustering for image clustering,” Pattern Recognition Letters, vol. 107, 2018, pp. 131–136. https://doi.org/10.1016/j.patrec.2017.08.02310.1016/j.patrec.2017.08.023Search in Google Scholar

[25] X. Wang, Z. Lei, X. Guo, C. Zhang, H. Shi, and S. Z. Li, “Multi-view subspace clustering with intactness-aware similarity,” Pattern Recognition, vol. 6, no. 2, 2018, pp. 50–63. https://doi.org/10.1016/j.patcog.2018.09.00910.1016/j.patcog.2018.09.009Search in Google Scholar

[26] Y. Chen, and Z. Yi, “Locality-constrained least squares regression for subspace clustering,” Knowledge-Based Systems, vol. 163, 2019, pp. 51–56. https://doi.org/10.1016/j.knosys.2018.08.01410.1016/j.knosys.2018.08.014Search in Google Scholar

[27] Ł. Struski, J. Tabor, and P. Spurek, “Lossy compression approach to subspace clustering,” Information Sciences, vol. 435, 2018, pp. 161–183. https://doi.org/10.1016/j.ins.2017.12.05610.1016/j.ins.2017.12.056Search in Google Scholar

[28] D. L. Davies, and D. W. Bouldin, “A Cluster Separation Measure,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-1, no. 2, 1979, pp. 224–227. https://doi.org/10.1109/TPAMI.1979.476690910.1109/TPAMI.1979.4766909Search in Google Scholar

[29] N. Amjady, F. Keynia, and H. Zareipour, “Wind power prediction by a new forecast engine composed of modified hybrid neural network and enhanced particle swarm optimization,” Sustainable Energy, vol. 2, no. 3, 2011, pp. 265–276. https://doi.org/10.1109/TSTE.2011.211468010.1109/TSTE.2011.2114680Search in Google Scholar

[30] T. P. Latchoumi, K. Balamurugan, K. Dinesh, and T. P. Ezhilarasi, “Particle swarm optimization approach for waterjet cavitation peening,” Measurement, vol. 141, 2019, pp. 184–189. https://doi.org/10.1016/j.measurement.2019.04.04010.1016/j.measurement.2019.04.040Search in Google Scholar

[31] F. Korner-Nievergelt, T. Roth, S. von Felten, J. Guélat, B. Almasi, and P. Korner-Nievergelt, “Chapter 12: Markov chain Monte Carlo simulation,” in Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN, Academic Press, 2015, pp. 197–212. https://doi.org/10.1016/B978-0-12-801370-0.00012-510.1016/B978-0-12-801370-0.00012-5Search in Google Scholar

[32] IGMC. [Online] Available from: https://www.igmc.irSearch in Google Scholar

eISSN:
2255-8691
Language:
English