{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T02:09:47Z","timestamp":1778638187081,"version":"3.51.4"},"reference-count":32,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2019,5,1]],"date-time":"2019-05-01T00:00:00Z","timestamp":1556668800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Many real world problems have big data, including recorded fields and\/or attributes. In such cases, data mining requires dimension reduction techniques because there are serious challenges facing conventional clustering methods in dealing with big data. The subspace selection method is one of the most important dimension reduction techniques. In such methods, a selected set of subspaces is substituted for the general dataset of the problem and clustering is done using this set. This article introduces the Shared Subscribe Hyper Simulation Optimization (SUBHSO) algorithm to introduce the optimized cluster centres to a set of subspaces. SUBHSO uses an optimization loop for modifying and optimizing the coordinates of the cluster centres with the particle swarm optimization (PSO) and the fitness function calculation using the Monte Carlo simulation. The case study on the big data of Iran electricity market (IEM) has shown the improvement of the defined fitness function, which represents the cluster cohesion and separation relative to other dimension reduction algorithms.<\/jats:p>","DOI":"10.2478\/acss-2019-0007","type":"journal-article","created":{"date-parts":[[2019,6,22]],"date-time":"2019-06-22T05:31:04Z","timestamp":1561181464000},"page":"49-60","source":"Crossref","is-referenced-by-count":2,"title":["Shared Subscribe Hyper Simulation Optimization (SUBHSO) Algorithm for Clustering Big Data \u2013 Using Big Databases of Iran Electricity Market"],"prefix":"10.2478","volume":"24","author":[{"given":"Mesbaholdin","family":"Salami","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Central Tehran Branch , Islamic Azad University , Tehran , Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farzad Movahedi","family":"Sobhani","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Science and Research Branch , Islamic Azad University , Tehran , Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Sadegh","family":"Ghazizadeh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Abbaspour School of Engineering , Shahid Beheshti University , Tehran , Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2019,6,20]]},"reference":[{"key":"2026051301195503786_j_acss-2019-0007_ref_001_w2aab3b8c13b1b7b1ab1ab1Aa","doi-asserted-by":"crossref","unstructured":"[1] H. Chen, and Z. Mao, \u201cStudy on the failure probability of occupant evacuation with the method of Monte Carlo sampling,\u201d Procedia Engineering, vol. 211, 2018, pp. 55\u201362. https:\/\/doi.org\/10.1016\/j.proeng.2017.12.13710.1016\/j.proeng.2017.12.137","DOI":"10.1016\/j.proeng.2017.12.137"},{"key":"2026051301195503786_j_acss-2019-0007_ref_002_w2aab3b8c13b1b7b1ab1ab2Aa","doi-asserted-by":"crossref","unstructured":"[2] T. G. Penkova, \u201cPrincipal component analysis and cluster analysis for evaluating the natural andanthropogenic territory safety,\u201d Procedia Computer Science, vol. 112, 2017, pp. 99\u2013108. https:\/\/doi.org\/10.1016\/j.procs.2017.08.17910.1016\/j.procs.2017.08.179","DOI":"10.1016\/j.procs.2017.08.179"},{"key":"2026051301195503786_j_acss-2019-0007_ref_003_w2aab3b8c13b1b7b1ab1ab3Aa","doi-asserted-by":"crossref","unstructured":"[3] E. Vera, D. Lucio, L. A. F. Fernandes, and L. Velho, \u201cHough transform for real-time plane detection in depth images,\u201d Pattern Recognition Letters, vol. 103, 2018, pp. 8\u201315. https:\/\/doi.org\/10.1016\/j.patrec.2017.12.02710.1016\/j.patrec.2017.12.027","DOI":"10.1016\/j.patrec.2017.12.027"},{"key":"2026051301195503786_j_acss-2019-0007_ref_004_w2aab3b8c13b1b7b1ab1ab4Aa","doi-asserted-by":"crossref","unstructured":"[4] M. H. Yang, J. H. Li, and B. X. Liu, \u201cFractal analysis on the cluster network in metallic liquid and glass,\u201d Journal of Alloys and Compounds, vol. 757, 2018, pp. 228\u2013232. https:\/\/doi.org\/10.1016\/j.jallcom.2018.05.06910.1016\/j.jallcom.2018.05.069","DOI":"10.1016\/j.jallcom.2018.05.069"},{"key":"2026051301195503786_j_acss-2019-0007_ref_005_w2aab3b8c13b1b7b1ab1ab5Aa","doi-asserted-by":"crossref","unstructured":"[5] T. Cui, F. Caravelli, and C. Ududec, \u201cCorrelations and clustering in wholesale electricity markets,\u201d Physica A: Statistical Mechanics and its Applications, vol. 492, 2018, pp. 1507\u20131522. https:\/\/doi.org\/10.1016\/j.physa.2017.11.07710.1016\/j.physa.2017.11.077","DOI":"10.1016\/j.physa.2017.11.077"},{"key":"2026051301195503786_j_acss-2019-0007_ref_006_w2aab3b8c13b1b7b1ab1ab6Aa","doi-asserted-by":"crossref","unstructured":"[6] G. Zhu, J. Wang, and H. Lu, \u201cClustering based ensemble correlation tracking,\u201d Computer Vision and Image Understanding, vol. 153, 2016, pp. 55\u201363. https:\/\/doi.org\/10.1016\/j.cviu.2016.05.00610.1016\/j.cviu.2016.05.006","DOI":"10.1016\/j.cviu.2016.05.006"},{"key":"2026051301195503786_j_acss-2019-0007_ref_007_w2aab3b8c13b1b7b1ab1ab7Aa","doi-asserted-by":"crossref","unstructured":"[7] S. Chormunge, and S. Jena, \u201cCorrelation based feature selection with clustering for high dimensional data,\u201d Journal of Electrical Systems and Information Technology, vol. 5, no. 3, 2018, pp. 542\u2013549. https:\/\/doi.org\/10.1016\/j.jesit.2017.06.00410.1016\/j.jesit.2017.06.004","DOI":"10.1016\/j.jesit.2017.06.004"},{"key":"2026051301195503786_j_acss-2019-0007_ref_008_w2aab3b8c13b1b7b1ab1ab8Aa","doi-asserted-by":"crossref","unstructured":"[8] K. Fujiwara, M. Kano, and S. Hasebe, \u201cDevelopment of correlation-based clustering method and its application to software sensing,\u201d Chemometrics and Intelligent Laboratory Systems, vol. 101, no. 2, 2010, pp. 130\u2013138. https:\/\/doi.org\/10.1016\/j.chemolab.2010.02.00610.1016\/j.chemolab.2010.02.006","DOI":"10.1016\/j.chemolab.2010.02.006"},{"key":"2026051301195503786_j_acss-2019-0007_ref_009_w2aab3b8c13b1b7b1ab1ab9Aa","doi-asserted-by":"crossref","unstructured":"[9] R. Veroneze, A. Banerjee, and F. J. von Zuben, \u201cEnumerating all maximal biclusters in numerical datasets,\u201d Information Sciences, vol. 379, 2017, pp. 288\u2013309. https:\/\/doi.org\/10.1016\/j.ins.2016.10.02910.1016\/j.ins.2016.10.029","DOI":"10.1016\/j.ins.2016.10.029"},{"key":"2026051301195503786_j_acss-2019-0007_ref_010_w2aab3b8c13b1b7b1ab1ac10Aa","doi-asserted-by":"crossref","unstructured":"[10] S. Chen, J. Liu, and T. Zeng, \u201cMeasuring the quality of linear patterns inbiclusters,\u201d Methods, vol. 83, 2015, pp. 18\u201327. https:\/\/doi.org\/10.1016\/j.ymeth.2015.04.00510.1016\/j.ymeth.2015.04.00525890245","DOI":"10.1016\/j.ymeth.2015.04.005"},{"key":"2026051301195503786_j_acss-2019-0007_ref_011_w2aab3b8c13b1b7b1ab1ac11Aa","doi-asserted-by":"crossref","unstructured":"[11] G. F. de Sousa Filho, L. dos A. F. Cabral, L. S. Ochi, and F. Protti, \u201cHybrid metaheuristic for bicluster editing problem,\u201d Electronic Notes in Discrete Mathematics, vol. 39, 2012, pp. 35\u201342. https:\/\/doi.org\/10.1016\/j.endm.2012.10.00610.1016\/j.endm.2012.10.006","DOI":"10.1016\/j.endm.2012.10.006"},{"key":"2026051301195503786_j_acss-2019-0007_ref_012_w2aab3b8c13b1b7b1ab1ac12Aa","doi-asserted-by":"crossref","unstructured":"[12] M. Wang, X. Shang, X. Li, W. Liu, and Z. Li, \u201cEfficient mining differential co-expression biclusters in microarray datasets,\u201d Gene, vol. 518, no. 1, 2013, pp. 59\u201369. https:\/\/doi.org\/10.1016\/j.gene.2012.11.08510.1016\/j.gene.2012.11.08523276708","DOI":"10.1016\/j.gene.2012.11.085"},{"key":"2026051301195503786_j_acss-2019-0007_ref_013_w2aab3b8c13b1b7b1ab1ac13Aa","doi-asserted-by":"crossref","unstructured":"[13] Y. Lee, J. Lee, and C. H. Jun, \u201cStability-based validation of bicluster solutions,\u201d Pattern Recognition, vol. 44, no. 2, 2011, pp. 252\u2013264. https:\/\/doi.org\/10.1016\/j.patcog.2010.08.02910.1016\/j.patcog.2010.08.029","DOI":"10.1016\/j.patcog.2010.08.029"},{"key":"2026051301195503786_j_acss-2019-0007_ref_014_w2aab3b8c13b1b7b1ab1ac14Aa","doi-asserted-by":"crossref","unstructured":"[14] F. Divina, B. Pontes, R. Gir\u00e1ldez, and J. S. Aguilar-Ruiz, \u201cAn effective measure for assessing the quality of biclusters,\u201d Computers in Biology and Medicine, vol. 42, no. 2, 2012, pp. 245\u2013256. https:\/\/doi.org\/10.1016\/j.compbiomed.2011.11.01510.1016\/j.compbiomed.2011.11.01522196882","DOI":"10.1016\/j.compbiomed.2011.11.015"},{"key":"2026051301195503786_j_acss-2019-0007_ref_015_w2aab3b8c13b1b7b1ab1ac15Aa","doi-asserted-by":"crossref","unstructured":"[15] C. C. Aggarwal, J. L. Wolf, P. S. Yu, C. Procopiuc, and J. S. Park, \u201cFast algorithms for projected clustering,\u201d Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, SIGMOD, ACM, New York, NY, USA, 1999, pp. 61\u201372. https:\/\/doi.org\/10.1145\/304181.30418810.1145\/304181.304188","DOI":"10.1145\/304182.304188"},{"key":"2026051301195503786_j_acss-2019-0007_ref_016_w2aab3b8c13b1b7b1ab1ac16Aa","doi-asserted-by":"crossref","unstructured":"[16] G. Moise, J. Sander, and M. Ester, \u201cRobust projected clustering,\u201d Knowledge and Information Systems, vol. 14, no. 3, 2008, pp. 273\u2013298. https:\/\/doi.org\/10.1007\/s10115-007-0090-610.1007\/s10115-007-0090-6","DOI":"10.1007\/s10115-007-0090-6"},{"key":"2026051301195503786_j_acss-2019-0007_ref_017_w2aab3b8c13b1b7b1ab1ac17Aa","doi-asserted-by":"crossref","unstructured":"[17] G. Gan, and J. Wu, \u201cA convergence theorem for the fuzzy subspace clustering (fsc) algorithm,\u201d Pattern Recognition, vol. 6, no. 2, 2008, pp. 1939\u20131947. https:\/\/doi.org\/10.1016\/j.patcog.2007.11.01110.1016\/j.patcog.2007.11.011","DOI":"10.1016\/j.patcog.2007.11.011"},{"key":"2026051301195503786_j_acss-2019-0007_ref_018_w2aab3b8c13b1b7b1ab1ac18Aa","doi-asserted-by":"crossref","unstructured":"[18] Z. Deng, K. S. Choi, F. L. Chung, and S. Wang, \u201cEnhanced soft subspace clustering integrating within-cluster and between-cluster information,\u201d Pattern Recognition, vol. 43, no. 3, 2010, pp. 767\u2013781. https:\/\/doi.org\/10.1016\/j.patcog.2009.09.01010.1016\/j.patcog.2009.09.010","DOI":"10.1016\/j.patcog.2009.09.010"},{"key":"2026051301195503786_j_acss-2019-0007_ref_019_w2aab3b8c13b1b7b1ab1ac19Aa","doi-asserted-by":"crossref","unstructured":"[19] X. Chen, Y. Ye, X. Xu, and J. Z. Huang, \u201cA feature group weighting method for subspace clustering of high-dimensional data,\u201d Pattern Recognition, vol. 45, no. 1, 2012, pp. 434\u2013446. https:\/\/doi.org\/10.1016\/j.patcog.2011.06.00410.1016\/j.patcog.2011.06.004","DOI":"10.1016\/j.patcog.2011.06.004"},{"key":"2026051301195503786_j_acss-2019-0007_ref_020_w2aab3b8c13b1b7b1ab1ac20Aa","doi-asserted-by":"crossref","unstructured":"[20] D. S. Modha, and W. S. Spangler, \u201cFeature weighting in k-means clustering,\u201d Machine Learning, vol. 52, no. 3, 2003, pp. 217\u2013237. https:\/\/doi.org\/10.1023\/A:102401660952810.1023\/A:1024016609528","DOI":"10.1023\/A:1024016609528"},{"key":"2026051301195503786_j_acss-2019-0007_ref_021_w2aab3b8c13b1b7b1ab1ac21Aa","doi-asserted-by":"crossref","unstructured":"[21] C. Domeniconi, D. Gunopulos, S. Ma, B. Yan, M. Al-Razgan, and D. Papadopoulos, \u201cLocally adaptive metrics for clustering high dimensional data,\u201d Data Mining and Knowledge Discovery, vol. 14, no. 1, 2007, pp. 63\u201397. https:\/\/doi.org\/10.1007\/s10618-006-0060-810.1007\/s10618-006-0060-8","DOI":"10.1007\/s10618-006-0060-8"},{"key":"2026051301195503786_j_acss-2019-0007_ref_022_w2aab3b8c13b1b7b1ab1ac22Aa","doi-asserted-by":"crossref","unstructured":"[22] Y. Zhu, K. M. Ting, and M. J. Carman, \u201cGrouping points by shared subspaces for effective subspace clustering,\u201d Pattern Recognition, vol. 83, 2018, pp. 230\u2013244. https:\/\/doi.org\/10.1016\/j.patcog.2018.05.02710.1016\/j.patcog.2018.05.027","DOI":"10.1016\/j.patcog.2018.05.027"},{"key":"2026051301195503786_j_acss-2019-0007_ref_023_w2aab3b8c13b1b7b1ab1ac23Aa","doi-asserted-by":"crossref","unstructured":"[23] H. Chen, W. Wang, and X. Feng, \u201cStructured sparse subspace clustering with within-cluster grouping,\u201d Pattern Recognition, vol. 83, 2018, pp. 107\u2013118. https:\/\/doi.org\/10.1016\/j.patcog.2018.05.02010.1016\/j.patcog.2018.05.020","DOI":"10.1016\/j.patcog.2018.05.020"},{"key":"2026051301195503786_j_acss-2019-0007_ref_024_w2aab3b8c13b1b7b1ab1ac24Aa","doi-asserted-by":"crossref","unstructured":"[24] W. Zhu, J. Lu, and J. Zhou, \u201cNonlinear subspace clustering for image clustering,\u201d Pattern Recognition Letters, vol. 107, 2018, pp. 131\u2013136. https:\/\/doi.org\/10.1016\/j.patrec.2017.08.02310.1016\/j.patrec.2017.08.023","DOI":"10.1016\/j.patrec.2017.08.023"},{"key":"2026051301195503786_j_acss-2019-0007_ref_025_w2aab3b8c13b1b7b1ab1ac25Aa","doi-asserted-by":"crossref","unstructured":"[25] X. Wang, Z. Lei, X. Guo, C. Zhang, H. Shi, and S. Z. Li, \u201cMulti-view subspace clustering with intactness-aware similarity,\u201d Pattern Recognition, vol. 6, no. 2, 2018, pp. 50\u201363. https:\/\/doi.org\/10.1016\/j.patcog.2018.09.00910.1016\/j.patcog.2018.09.009","DOI":"10.1016\/j.patcog.2018.09.009"},{"key":"2026051301195503786_j_acss-2019-0007_ref_026_w2aab3b8c13b1b7b1ab1ac26Aa","doi-asserted-by":"crossref","unstructured":"[26] Y. Chen, and Z. Yi, \u201cLocality-constrained least squares regression for subspace clustering,\u201d Knowledge-Based Systems, vol. 163, 2019, pp. 51\u201356. https:\/\/doi.org\/10.1016\/j.knosys.2018.08.01410.1016\/j.knosys.2018.08.014","DOI":"10.1016\/j.knosys.2018.08.014"},{"key":"2026051301195503786_j_acss-2019-0007_ref_027_w2aab3b8c13b1b7b1ab1ac27Aa","doi-asserted-by":"crossref","unstructured":"[27] \u0141. Struski, J. Tabor, and P. Spurek, \u201cLossy compression approach to subspace clustering,\u201d Information Sciences, vol. 435, 2018, pp. 161\u2013183. https:\/\/doi.org\/10.1016\/j.ins.2017.12.05610.1016\/j.ins.2017.12.056","DOI":"10.1016\/j.ins.2017.12.056"},{"key":"2026051301195503786_j_acss-2019-0007_ref_028_w2aab3b8c13b1b7b1ab1ac28Aa","doi-asserted-by":"crossref","unstructured":"[28] D. L. Davies, and D. W. Bouldin, \u201cA Cluster Separation Measure,\u201d IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-1, no. 2, 1979, pp. 224\u2013227. https:\/\/doi.org\/10.1109\/TPAMI.1979.476690910.1109\/TPAMI.1979.4766909","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"2026051301195503786_j_acss-2019-0007_ref_029_w2aab3b8c13b1b7b1ab1ac29Aa","doi-asserted-by":"crossref","unstructured":"[29] N. Amjady, F. Keynia, and H. Zareipour, \u201cWind power prediction by a new forecast engine composed of modified hybrid neural network and enhanced particle swarm optimization,\u201d Sustainable Energy, vol. 2, no. 3, 2011, pp. 265\u2013276. https:\/\/doi.org\/10.1109\/TSTE.2011.211468010.1109\/TSTE.2011.2114680","DOI":"10.1109\/TSTE.2011.2114680"},{"key":"2026051301195503786_j_acss-2019-0007_ref_030_w2aab3b8c13b1b7b1ab1ac30Aa","doi-asserted-by":"crossref","unstructured":"[30] T. P. Latchoumi, K. Balamurugan, K. Dinesh, and T. P. Ezhilarasi, \u201cParticle swarm optimization approach for waterjet cavitation peening,\u201d Measurement, vol. 141, 2019, pp. 184\u2013189. https:\/\/doi.org\/10.1016\/j.measurement.2019.04.04010.1016\/j.measurement.2019.04.040","DOI":"10.1016\/j.measurement.2019.04.040"},{"key":"2026051301195503786_j_acss-2019-0007_ref_031_w2aab3b8c13b1b7b1ab1ac31Aa","doi-asserted-by":"crossref","unstructured":"[31] F. Korner-Nievergelt, T. Roth, S. von Felten, J. Gu\u00e9lat, B. Almasi, and P. Korner-Nievergelt, \u201cChapter 12: Markov chain Monte Carlo simulation,\u201d in Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN, Academic Press, 2015, pp. 197\u2013212. https:\/\/doi.org\/10.1016\/B978-0-12-801370-0.00012-510.1016\/B978-0-12-801370-0.00012-5","DOI":"10.1016\/B978-0-12-801370-0.00012-5"},{"key":"2026051301195503786_j_acss-2019-0007_ref_032_w2aab3b8c13b1b7b1ab1ac32Aa","unstructured":"[32] IGMC. [Online] Available from: https:\/\/www.igmc.ir"}],"container-title":["Applied Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/content.sciendo.com\/view\/journals\/acss\/24\/1\/article-p49.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/acss-2019-0007","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:20:22Z","timestamp":1778635222000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/acss-2019-0007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,1]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,6,20]]},"published-print":{"date-parts":[[2019,5,1]]}},"alternative-id":["10.2478\/acss-2019-0007"],"URL":"https:\/\/doi.org\/10.2478\/acss-2019-0007","relation":{},"ISSN":["2255-8691"],"issn-type":[{"value":"2255-8691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,1]]}}}