{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T12:01:56Z","timestamp":1747224116670,"version":"3.40.5"},"reference-count":34,"publisher":"IGI Global","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<p>The authors present the first clustering algorithm for use with distributed data that is fast, reliable, and does not make any presumptions in terms of data distribution. The authors' algorithm constructs a global clustering model using small local models received from local clustering statistics. This approach outperforms the classical non-distributed approaches since it does not require downloading all of the data to the central processing unit. The authors' solution is a hybrid algorithm that uses the best partitioning and density-based approach. The proposed algorithm handles uneven data dispersion without a transfer overload of additional data. Experiments were carried out with large datasets and these showed that the proposed solution introduces no loss of quality compared to non-distributed approaches and can achieve even better results, approaching reference clustering. This is an excellent outcome, considering that the algorithm can only build a model from fragmented data where the communication cost between nodes is negligible.<\/p>","DOI":"10.4018\/ijdwm.2019100101","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T14:16:59Z","timestamp":1568384219000},"page":"1-20","source":"Crossref","is-referenced-by-count":2,"title":["Hybrid Partitioning-Density Algorithm for K-Means Clustering of Distributed Data Utilizing OPTICS"],"prefix":"10.4018","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4229-8098","authenticated-orcid":true,"given":"Miko\u0142aj","family":"Markiewicz","sequence":"first","affiliation":[{"name":"Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland"}]},{"given":"Jakub","family":"Koperwas","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland"}]}],"member":"2432","reference":[{"key":"IJDWM.2019100101-0","doi-asserted-by":"publisher","DOI":"10.1145\/304181.304187"},{"key":"IJDWM.2019100101-1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00013"},{"key":"IJDWM.2019100101-2","doi-asserted-by":"publisher","DOI":"10.14778\/2180912.2180915"},{"key":"IJDWM.2019100101-3","article-title":"Lightning fast asynchronous distributed k-means clustering.","author":"\u00c1.Berta","year":"2014","journal-title":"Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning"},{"key":"IJDWM.2019100101-4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23644-0_8"},{"key":"IJDWM.2019100101-5","first-page":"9","article-title":"Scaling Clustering Algorithms to Large Databases.","volume":"98","author":"P. S.Bradley","year":"1998","journal-title":"KDD: Proceedings \/ International Conference on Knowledge Discovery & Data Mining. International Conference on Knowledge Discovery & Data Mining"},{"key":"IJDWM.2019100101-6","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/431047"},{"key":"IJDWM.2019100101-7","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-013-0489-0"},{"key":"IJDWM.2019100101-8","unstructured":"Cisco. (2018, February). Cisco Global Cloud Index: Forecast and Methodology, 2016\u20132021 White Paper. Retrieved from https:\/\/www.cisco.com\/c\/en\/us\/solutions\/collateral\/service-provider\/global-cloud-index-gci\/white-paper-c11-738085.html"},{"key":"IJDWM.2019100101-9","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972764.14"},{"key":"IJDWM.2019100101-10","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327492"},{"key":"IJDWM.2019100101-11","unstructured":"Ester, M., Kriegel, H. P., Sander, J., Wimmer, M., & Xu, X. Incremental clustering for mining in a data ware housing. University of Munich Oettingenstr."},{"issue":"34","key":"IJDWM.2019100101-12","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise.","volume":"96","author":"M.Ester","year":"1996","journal-title":"KDD: Proceedings \/ International Conference on Knowledge Discovery & Data Mining. International Conference on Knowledge Discovery & Data Mining"},{"key":"IJDWM.2019100101-13","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1216"},{"issue":"1","key":"IJDWM.2019100101-14","first-page":"100","article-title":"Algorithm AS 136: A k-means clustering algorithm.","volume":"28","author":"J. A.Hartigan","year":"1979","journal-title":"Journal of the Royal Statistical Society. Series C, Applied Statistics"},{"key":"IJDWM.2019100101-15","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2014.07.006"},{"key":"IJDWM.2019100101-16","first-page":"58","article-title":"An efficient approach to clustering in large multimedia databases with noise.","volume":"98","author":"A.Hinneburg","year":"1998","journal-title":"KDD: Proceedings \/ International Conference on Knowledge Discovery & Data Mining. International Conference on Knowledge Discovery & Data Mining"},{"key":"IJDWM.2019100101-17","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2247592"},{"key":"IJDWM.2019100101-18","doi-asserted-by":"publisher","DOI":"10.1007\/BF01908075"},{"key":"IJDWM.2019100101-19","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972764.47"},{"key":"IJDWM.2019100101-20","first-page":"88","article-title":"DBDC: Density based distributed clustering.","author":"E.Januzaj","year":"2004","journal-title":"International Conference on Extending Database Technology"},{"key":"IJDWM.2019100101-21","first-page":"312","article-title":"Ensemble learning based distributed clustering.","author":"G.Ji","year":"2007","journal-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining"},{"key":"IJDWM.2019100101-22","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-005-0210-0"},{"key":"IJDWM.2019100101-23","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2004.45"},{"key":"IJDWM.2019100101-24","unstructured":"Kashyap, H., Ahmed, H. A., Hoque, N., Roy, S., & Bhattacharyya, D. K. (2015). Big data analytics in bioinformatics: A machine learning perspective. arXiv:1506.05101"},{"key":"IJDWM.2019100101-25","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-36561-3_5"},{"key":"IJDWM.2019100101-26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-16515-3_21"},{"key":"IJDWM.2019100101-27","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2004.1320061"},{"key":"IJDWM.2019100101-28","unstructured":"Oliva, G., Setola, R., & Hadjicostis, C.N. (2013). Distributed k-means algorithm. arXiv:1312.4176"},{"key":"IJDWM.2019100101-29","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC.2017.8024694"},{"key":"IJDWM.2019100101-30","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2014.2318177"},{"issue":"4","key":"IJDWM.2019100101-31","first-page":"168","article-title":"Impact of normalization in distributed k-means clustering.","volume":"4","author":"N. K.Visalakshi","year":"2009","journal-title":"International Journal of Soft Computing"},{"key":"IJDWM.2019100101-32","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(97)00077-8"},{"journal-title":"Understanding big data: Analytics for enterprise class hadoop and streaming data","year":"2011","author":"P.Zikopoulos","key":"IJDWM.2019100101-33"}],"container-title":["International Journal of Data Warehousing and Mining"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=237135","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T21:49:14Z","timestamp":1651873754000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJDWM.2019100101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2019,10,1]]},"references-count":34,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,10]]}},"URL":"https:\/\/doi.org\/10.4018\/ijdwm.2019100101","relation":{},"ISSN":["1548-3924","1548-3932"],"issn-type":[{"type":"print","value":"1548-3924"},{"type":"electronic","value":"1548-3932"}],"subject":[],"published":{"date-parts":[[2019,10,1]]}}}