{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:23:07Z","timestamp":1772724187273,"version":"3.50.1"},"reference-count":19,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2012,3]]},"abstract":"<jats:p>Clustering uncertain data has emerged as a challenging task in uncertain data management and mining. Thanks to a computational complexity advantage over other clustering paradigms, partitional clustering has been particularly studied and a number of algorithms have been developed. While existing proposals differ mainly in the notions of cluster centroid and clustering objective function, little attention has been given to an analysis of their characteristics and limits. In this work, we theoretically investigate major existing methods of partitional clustering, and alternatively propose a well-founded approach to clustering uncertain data based on a novel notion of cluster centroid. A cluster centroid is seen as an uncertain object defined in terms of a random variable whose realizations are derived based on all deterministic representations of the objects to be clustered. As demonstrated theoretically and experimentally, this allows for better representing a cluster of uncertain objects, thus supporting a consistently improved clustering performance while maintaining comparable efficiency with existing partitional clustering algorithms.<\/jats:p>","DOI":"10.14778\/2180912.2180914","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T08:17:57Z","timestamp":1403597877000},"page":"610-621","source":"Crossref","is-referenced-by-count":22,"title":["Uncertain centroid based partitional clustering of uncertain data"],"prefix":"10.14778","volume":"5","author":[{"given":"Francesco","family":"Gullo","sequence":"first","affiliation":[{"name":"Yahoo! Research, Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Tagarelli","sequence":"additional","affiliation":[{"name":"University of Calabria, Rende (CS), Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/1513922"},{"key":"e_1_2_1_2_1","unstructured":"A. Asuncion and D. Newman. UCI Machine Learning Repository http:\/\/archive.ics.uci.edu\/ml\/.  A. Asuncion and D. Newman. UCI Machine Learning Repository http:\/\/archive.ics.uci.edu\/ml\/."},{"key":"e_1_2_1_3_1","unstructured":"Broad Institute of MIT and Harvard. Cancer program dataset page http:\/\/www.broad.mit.edu\/cgibin\/cancer\/datasets.cgi.  Broad Institute of MIT and Harvard. 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