Have a personal or library account? Click to login
Shared Subscribe Hyper Simulation Optimization (SUBHSO) Algorithm for Clustering Big Data – Using Big Databases of Iran Electricity Market Cover

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

Applied Computer Systems
Volume 24 (2019): Issue 1 (May 2019)
By: Mesbaholdin Salami,  Farzad Movahedi Sobhani and  Mohammad Sadegh Ghazizadeh  
Open Access
|Jun 2019

References

  1. [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.2017.12.137
    Search in Google ScholarBack to article
  2. [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.2017.08.179
    Search in Google ScholarBack to article
  3. [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.2017.12.027
    Search in Google ScholarBack to article
  4. [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.2018.05.069
    Search in Google ScholarBack to article
  5. [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.2017.11.077
    Search in Google ScholarBack to article
  6. [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.2016.05.006
    Search in Google ScholarBack to article
  7. [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.2017.06.004
    Search in Google ScholarBack to article
  8. [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.2010.02.006
    Search in Google ScholarBack to article
  9. [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.2016.10.029
    Search in Google ScholarBack to article
  10. [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.2015.04.00525890245
    Search in Google ScholarBack to article
  11. [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.2012.10.006
    Search in Google ScholarBack to article
  12. [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.2012.11.08523276708
    Search in Google ScholarBack to article
  13. [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.2010.08.029
    Search in Google ScholarBack to article
  14. [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.2011.11.01522196882
    Search in Google ScholarBack to article
  15. [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/10.1145/304181.30418810.1145/304181.304188
    Search in Google ScholarBack to article
  16. [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/s10115-007-0090-6
    Search in Google ScholarBack to article
  17. [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.2007.11.011
    Search in Google ScholarBack to article
  18. [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.2009.09.010
    Search in Google ScholarBack to article
  19. [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.2011.06.004
    Search in Google ScholarBack to article
  20. [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:1024016609528
    Search in Google ScholarBack to article
  21. [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/s10618-006-0060-8
    Search in Google ScholarBack to article
  22. [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.2018.05.027
    Search in Google ScholarBack to article
  23. [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.2018.05.020
    Search in Google ScholarBack to article
  24. [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.2017.08.023
    Search in Google ScholarBack to article
  25. [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.2018.09.009
    Search in Google ScholarBack to article
  26. [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.2018.08.014
    Search in Google ScholarBack to article
  27. [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.2017.12.056
    Search in Google ScholarBack to article
  28. [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.1979.4766909
    Search in Google ScholarBack to article
  29. [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.2011.2114680
    Search in Google ScholarBack to article
  30. [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.2019.04.040
    Search in Google ScholarBack to article
  31. [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/B978-0-12-801370-0.00012-5
    Search in Google ScholarBack to article
  32. [32] IGMC. [Online] Available from: https://www.igmc.ir
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/acss-2019-0007 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683
Journal RSS Feed
Language: English
Page range: 49 - 60
Published on: Jun 20, 2019
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
Clustering,
electricity demand forecasting,
Monte Carlo simulation,
particle swarm optimization,
subspace search method
Related subjects:
Computer sciences,
Artificial intelligence,
Information technology,
Project management,
Software development

© 2019 Mesbaholdin Salami, Farzad Movahedi Sobhani, Mohammad Sadegh Ghazizadeh, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous articleVolume 24 (2019): Issue 1 (May 2019)Next article