{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:31:07Z","timestamp":1777491067804,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2013,8,1]],"date-time":"2013-08-01T00:00:00Z","timestamp":1375315200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Concurrent Engineering"],"published-print":{"date-parts":[[2013,9]]},"abstract":"<jats:p>\n                    Exascale systems are the next frontier in high-performance computing and are expected to deliver a performance of the order of 10\n                    <jats:sup>18<\/jats:sup>\n                    operations per second using massive multicore processors. Very large- and extreme-scale parallel systems pose critical algorithmic challenges, especially related to concurrency, locality and the need to avoid global communication patterns. This work investigates a novel protocol for dynamic group communication that can be used to remove the global communication requirement and to reduce the communication cost in parallel formulations of iterative data mining algorithms. The protocol is used to provide a communication-efficient parallel formulation of the k-means algorithm for cluster analysis. The approach is based on a collective communication operation for dynamic groups of processes and exploits non-uniform data distributions. Non-uniform data distributions can be either found in real-world distributed applications or induced by means of multidimensional binary search trees. The analysis of the proposed dynamic group communication protocol has shown that it does not introduce significant communication overhead. The parallel clustering algorithm has also been extended to accommodate an approximation error, which allows a further reduction of the communication costs. The effectiveness of the exact and approximate methods has been tested in a parallel computing system with 64 processors and in simulations with 1024 processing elements.\n                  <\/jats:p>","DOI":"10.1177\/1063293x13495551","type":"journal-article","created":{"date-parts":[[2013,8,1]],"date-time":"2013-08-01T20:40:49Z","timestamp":1375389649000},"page":"227-234","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic group communication for large-scale parallel data mining"],"prefix":"10.1177","volume":"21","author":[{"given":"Amogh","family":"Katti","sequence":"first","affiliation":[{"name":"School of Systems Engineering, University of Reading, Reading, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuseppe","family":"Di Fatta","sequence":"additional","affiliation":[{"name":"School of Systems Engineering, University of Reading, Reading, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2013,8,1]]},"reference":[{"key":"bibr1-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5103-0"},{"key":"bibr2-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48912-6_47"},{"key":"bibr3-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1109\/CLUSTR.2006.311890"},{"key":"bibr4-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1145\/277697.277739"},{"key":"bibr5-1063293X13495551","first-page":"245","volume-title":"Large-Scale Parallel Data Mining (Lecture Notes in Computer Science, vol. 1759)","author":"Dhillon IS","year":"2000"},{"key":"bibr6-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1109\/CIT.2010.424"},{"key":"bibr7-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2012.09.009"},{"key":"bibr8-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24449-0_32"},{"key":"bibr9-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45591-4_51"},{"key":"bibr10-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03770-2_18"},{"key":"bibr11-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2009.09.011"},{"key":"bibr12-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1145\/331499.331504"},{"key":"bibr13-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1109\/34.709614"},{"key":"bibr14-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017616"},{"key":"bibr15-1063293X13495551","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"bibr16-1063293X13495551","first-page":"281","volume-title":"Proceedings of the 5th Berkeley symposium on mathematical statistics and probability","volume":"1","author":"MacQueen JB","year":"1967"},{"key":"bibr17-1063293X13495551","unstructured":"Moore AW (1990) Efficient memory-based learning for robot control. 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