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Syst."],"published-print":{"date-parts":[[2016,7,25]]},"abstract":"<jats:p>\n            Many algorithms for clustering data streams that are based on the widely used\n            <jats:italic>k<\/jats:italic>\n            -Means have been proposed in the literature. Most of these algorithms assume that the number of clusters,\n            <jats:italic>k<\/jats:italic>\n            , is known and fixed\n            <jats:italic>a priori<\/jats:italic>\n            by the user. Aimed at relaxing this assumption, which is often unrealistic in practical applications, we propose a support system that allows not only estimating the number of clusters automatically from data but also monitoring the process of the data-stream clustering. We illustrate the potential of the proposed system by means of a prototype that implements eight algorithms for clustering data streams, namely, Stream LSearch-OMR\n            <jats:italic>k<\/jats:italic>\n            , Stream LSearch-B\n            <jats:italic>k<\/jats:italic>\n            M, Stream LSearch-IOMR\n            <jats:italic>k<\/jats:italic>\n            , Stream LSearch-IB\n            <jats:italic>k<\/jats:italic>\n            M, CluStream-OMR\n            <jats:italic>k<\/jats:italic>\n            , CluStream-B\n            <jats:italic>k<\/jats:italic>\n            M, StreamKM++-OMR\n            <jats:italic>k<\/jats:italic>\n            , and StreamKM++\u2212B\n            <jats:italic>k<\/jats:italic>\n            M. These algorithms are combinations of three state-of-the-art algorithms for clustering data streams with fixed\n            <jats:italic>k<\/jats:italic>\n            , namely, Stream LSearch, CluStream, and StreamKM++, with two algorithms for estimating the number of clusters, which are Ordered Multiple Runs of\n            <jats:italic>k<\/jats:italic>\n            -Means (OMR\n            <jats:italic>k<\/jats:italic>\n            ) and Bisecting\n            <jats:italic>k<\/jats:italic>\n            -Means (B\n            <jats:italic>k<\/jats:italic>\n            M). We experimentally compare the performance of these algorithms using both synthetic and real-world data streams. Analyses of statistical significance suggest that the algorithms that are based on OMR\n            <jats:italic>k<\/jats:italic>\n            yield the best data partitions, while the algorithms that are based on B\n            <jats:italic>k<\/jats:italic>\n            M are more computationally efficient. Additionally, StreamKM++\u2212OMR\n            <jats:italic>k<\/jats:italic>\n            and Stream LSearch-IB\n            <jats:italic>k<\/jats:italic>\n            M provide the best tradeoff relationship between accuracy and efficiency.\n          <\/jats:p>","DOI":"10.1145\/2932704","type":"journal-article","created":{"date-parts":[[2016,7,26]],"date-time":"2016-07-26T13:27:29Z","timestamp":1469539649000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["A Support System for Clustering Data Streams with a Variable Number of Clusters"],"prefix":"10.1145","volume":"11","author":[{"given":"Jonathan de Andrade","family":"Silva","sequence":"first","affiliation":[{"name":"University of S\u00e3o Paulo (USP), MS, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo Raul","family":"Hruschka","sequence":"additional","affiliation":[{"name":"University of S\u00e3o Paulo (USP), SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,7,25]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/2790231.2790247"},{"key":"e_1_2_1_2_1","volume-title":"Yu","author":"Aggarwal Charu C.","year":"2003","unstructured":"Charu C. 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