{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T04:12:42Z","timestamp":1747195962452,"version":"3.40.5"},"reference-count":39,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2021,1,11]],"date-time":"2021-01-11T00:00:00Z","timestamp":1610323200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The vertical collaborative clustering aims to unravel the hidden structure of data (similarity) among different sites, which will help data owners to make a smart decision without sharing actual data. For example, various hospitals located in different regions want to investigate the structure of common disease among people of different populations to identify latent causes without sharing actual data with other hospitals. Similarly, a chain of regional educational institutions wants to evaluate their students\u2019 performance belonging to different regions based on common latent constructs. The available methods used for finding hidden structures are complicated and biased to perform collaboration in measuring similarity among multiple sites. This study proposes vertical collaborative clustering using a bit plane slicing approach (VCC-BPS), which is simple and unique with improved accuracy, manages collaboration among various data sites. The VCC-BPS transforms data from input space to code space, capturing maximum similarity locally and collaboratively at a particular bit plane. The findings of this study highlight the significance of those particular bits which fit the model in correctly classifying class labels locally and collaboratively. Thenceforth, the data owner appraises local and collaborative results to reach a better decision. The VCC-BPS is validated by Geyser, Skin and Iris datasets and its results are compared with the composite dataset. It is found that the VCC-BPS outperforms existing solutions with improved accuracy in term of purity and Davies-Boulding index to manage collaboration among different data sites. It also performs data compression by representing a large number of observations with a small number of data symbols.<\/jats:p>","DOI":"10.1371\/journal.pone.0244691","type":"journal-article","created":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T11:22:50Z","timestamp":1610450570000},"page":"e0244691","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["VCC-BPS: Vertical Collaborative Clustering using Bit Plane Slicing"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3680-1438","authenticated-orcid":true,"given":"WAQAR","family":"ISHAQ","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ELIYA","family":"BUYUKKAYA","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"MUSHTAQ","family":"ALI","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZAKIR","family":"KHAN","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,1,11]]},"reference":[{"key":"pone.0244691.ref001","doi-asserted-by":"crossref","unstructured":"Caruana, R., Karampatziakis, N., & Yessenalina, A. (2008, July). An empirical evaluation of supervised learning in high dimensions. In Proceedings of the 25th international conference on Machine learning (pp. 96-103). ACM.","DOI":"10.1145\/1390156.1390169"},{"volume-title":"Partitional clustering algorithms","year":"2014","author":"M. E Celebi","key":"pone.0244691.ref002"},{"issue":"6","key":"pone.0244691.ref003","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1109\/TPAMI.2005.113","article-title":"Combining multiple clusterings using evidence accumulation","volume":"27","author":"A. L. Fred","year":"2005","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"pone.0244691.ref004","doi-asserted-by":"crossref","unstructured":"W. Ishaq and E. Buyukkaya, \u201cDark patches in clustering,\u201d 2017 International Conference on Computer Science and Engineering (UBMK), Antalya, 2017, pp. 806-811.","DOI":"10.1109\/UBMK.2017.8093535"},{"key":"pone.0244691.ref005","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.inffus.2017.04.008","article-title":"Collaborative clustering: Why, when, what and how","volume":"39","author":"A. Cornuejols","year":"2018","journal-title":"Information Fusion"},{"key":"pone.0244691.ref006","doi-asserted-by":"crossref","unstructured":"P. Rastin, G. Cabanes, N. Grozavu and Y. Bennani, \u201cCollaborative Clustering: How to Select the Optimal Collaborators?,\u201d 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, 2015, pp. 787-794.","DOI":"10.1109\/SSCI.2015.117"},{"key":"pone.0244691.ref007","doi-asserted-by":"crossref","unstructured":"N. Grozavu, M. Ghassany, & Y. Bennani, (2011). Learning confidence exchange in collaborative clustering in Neural Networks (IJCNN), The 2011 International Joint Conference on, 5 (1), 872-879.","DOI":"10.1109\/IJCNN.2011.6033313"},{"key":"pone.0244691.ref008","unstructured":"Ghassany, M., Grozavu, N. & Bennani, Y. (2013), Collaborative multi-view clustering, in \u2018The 2013 International Joint Conference on Neural Networks (IJCNN)\u2019, pp. 1-8."},{"issue":"4","key":"pone.0244691.ref009","doi-asserted-by":"crossref","first-page":"245","DOI":"10.3233\/HIS-160219","article-title":"From horizontal to vertical collaborative clustering using generative topographic maps","volume":"12","author":"J. Sublime","year":"2015","journal-title":"International journal of hybrid intelligent systems"},{"issue":"1","key":"pone.0244691.ref010","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1162\/089976698300017953","article-title":"GTM: The generative topographic mapping","volume":"10","author":"C. M. Bishop","year":"1998","journal-title":"Neural computation"},{"key":"pone.0244691.ref011","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.patcog.2017.07.014","article-title":"Entropy Based Probabilistic Collaborative Clustering","volume":"vol. 72","author":"J. Sublime","year":"2017","journal-title":"Pattern Recognition"},{"key":"pone.0244691.ref012","doi-asserted-by":"crossref","unstructured":"J. Sublime, N. Grozavu, Y. Bennani and A. Cornu, \u201cVertical Collaborative Clustering using Generative Topographic Maps\u201d,In IEEE 7th International Conference on Soft Computing and Pattern Recognition, SocPaR 2015.","DOI":"10.1109\/SOCPAR.2015.7492807"},{"key":"pone.0244691.ref013","article-title":"Appropriate Learning Rate and Neighborhood Function of Self-organizing Map (SOM) for Specific Humidity Pattern Classification over Southern Thailand","author":"W. Natita","year":"2016","journal-title":"International Journal of Modeling and Optimization"},{"key":"pone.0244691.ref014","unstructured":"Bed Jatin and Toshniwal Durga, \u201cSFA-GTM: Seismic Facies Analysis Based on Generative Topographic Map and RBF\u201d,2018."},{"key":"pone.0244691.ref015","doi-asserted-by":"crossref","unstructured":"A. Filali, C. Jlassi and N. Arous, \u201cA Hybrid Collaborative Clustering Using Self-Organizing Map,\u201d 2017 IEEE\/ACS 14th International Conference on Computer Systems and Applications (AICCSA), Hammamet, 2017, pp. 709-716.","DOI":"10.1109\/AICCSA.2017.111"},{"key":"pone.0244691.ref016","unstructured":"Kohonen, T., Schroeder, M. R., Huang, T. S., & Maps, S. O. (2001). Springer-Verlag New York. Inc., Secaucus, NJ, 43(2)."},{"issue":"04","key":"pone.0244691.ref017","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1142\/S1469026805001684","article-title":"New self-organizing maps for multivariate sequences processing","volume":"5","author":"F. Zehraoui","year":"2005","journal-title":"International Journal of Computational Intelligence and Applications"},{"key":"pone.0244691.ref018","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/978-94-011-5014-9_12","volume-title":"Learning in graphical models","author":"R. M. Neal","year":"1998"},{"key":"pone.0244691.ref019","doi-asserted-by":"crossref","unstructured":"J. Sublime, B. Matei and P. Murena, \u201cAnalysis of the influence of diversity in collaborative and multi-view clustering,\u201d 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 2017, pp. 4126-4133.","DOI":"10.1109\/IJCNN.2017.7966377"},{"issue":"3","key":"pone.0244691.ref020","first-page":"3","article-title":"Generative or discriminative? getting the best of both worlds","volume":"8","author":"J. M. Bernardo","year":"2007","journal-title":"Bayesian statistics"},{"key":"pone.0244691.ref021","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781107298019","volume-title":"Understanding machine learning: From theory to algorithms","author":"S. Shalev-Shwartz","year":"2014"},{"key":"pone.0244691.ref022","unstructured":"Ackerman, M., Ben-David, S., & Loker, D. (2010). Towards property-based classification of clustering paradigms. In Advances in Neural Information Processing Systems (pp. 10-18)."},{"volume-title":"Springer Series in Statistics","year":"2009","author":"T. Hastie","key":"pone.0244691.ref023"},{"key":"pone.0244691.ref024","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/BF01908065","article-title":"On some significance tests in cluster analysis","volume":"2","author":"H.-H Bock","year":"1985","journal-title":"Journal of Classification"},{"issue":"2","key":"pone.0244691.ref025","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.datak.2009.10.004","article-title":"Collaborative clustering with background knowledge","volume":"69","author":"G. Forestier","year":"2010","journal-title":"Data & Knowledge Engineering"},{"key":"pone.0244691.ref026","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1038\/srep00336","article-title":"Consensus clustering in complex networks","volume":"2","author":"A. Lancichinetti","year":"2012","journal-title":"Scientific reports"},{"issue":"02","key":"pone.0244691.ref027","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1142\/S0218202513400125","article-title":"Consensus-like algorithms for estimation of Gaussian mixtures over large scale networks","volume":"24","author":"F. Fagnani","year":"2014","journal-title":"Mathematical Models and Methods in Applied Sciences"},{"issue":"1","key":"pone.0244691.ref028","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1145\/1217299.1217303","article-title":"Clustering aggregation","volume":"1","author":"A. Gionis","year":"2007","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"pone.0244691.ref029","doi-asserted-by":"crossref","unstructured":"J. Sublime, D. Maurel, N. Grozavu, B. Matei and Y. Bennani, \u201cOptimizing exchange confidence during collaborative clustering,\u201d 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, 2018, pp. 1-8.","DOI":"10.1109\/IJCNN.2018.8489758"},{"key":"pone.0244691.ref030","doi-asserted-by":"crossref","unstructured":"N. Grozavu, G. Cabanes and Y. Bennani, \u201cDiversity analysis in collaborative clustering,\u201d 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, 2014, pp. 1754-1761.","DOI":"10.1109\/IJCNN.2014.6889528"},{"journal-title":"International Journal of Computational Intelligence and Applications","article-title":"\u201cCollaborative clustering using prototype-based techniques\u201d 2012","author":"Ghassany Mohamad","key":"pone.0244691.ref031"},{"key":"pone.0244691.ref032","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.procs.2018.10.524","article-title":"\u201cTopological multi-view clustering for collaborative filtering\u201d 2018","volume":"144","author":"Falih Issam","journal-title":"Procedia Computer Science"},{"key":"pone.0244691.ref033","unstructured":"J. Kim, M. Sullivan, E. Choukse & M. Erez, (2016), Bit-Plane Compression: Transforming Data for Better Compression in Many-Core Architectures, ACM\/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), Seoul, pp. 329-340."},{"key":"pone.0244691.ref034","article-title":"Lossless image compression via bit-plane separation and multilayer context tree modeling","author":"Alexey Podlasov","year":"2006","journal-title":"J. Electronic Imaging"},{"key":"pone.0244691.ref035","article-title":"Gray Scale Image Compression using Bit Plane Slicing and Developed RLE Algorithms","author":"Hassan K. Albahadily","year":"2017","journal-title":"International Journal of Advanced Research in Computer and Communication Engineering"},{"volume-title":"Digital Image Processing","author":"Rafael C. Ganzalez","key":"pone.0244691.ref036"},{"key":"pone.0244691.ref037","doi-asserted-by":"crossref","first-page":"357","DOI":"10.2307\/2347385","article-title":"A look at some data on the Old Faithful geyser","volume":"39","author":"A Azzalini","year":"1990","journal-title":"Applied Statistics"},{"key":"pone.0244691.ref038","unstructured":"A. Frank & A. Asuncion, (2010) UCI machine learning repository, [Online]. Available: https:\/\/archive.ics.uci.edu\/ml\/datasets\/Skin+Segmentation"},{"key":"pone.0244691.ref039","unstructured":"A. Frank & A. Asuncion, (2010) UCI machine learning repository, [Online]. Available: http:\/\/archive.ics.uci.edu\/ml\/datasets\/Iris"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0244691","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T11:24:08Z","timestamp":1610450648000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0244691"}},"subtitle":[],"editor":[{"given":"Talib","family":"Al-Ameri","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1,11]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,1,11]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0244691","relation":{},"ISSN":["1932-6203"],"issn-type":[{"type":"electronic","value":"1932-6203"}],"subject":[],"published":{"date-parts":[[2021,1,11]]}}}