{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T02:56:28Z","timestamp":1706756188481},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,8,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Traditional clustering algorithms merely considered static data. Today's <jats:italic>various<\/jats:italic> applications and research issues\nin big data mining have however to deal with continuous, possibly infinite streams of data, arriving at high\n<jats:italic>velocity<\/jats:italic>. Web traffic data, surveillance data, sensor measurements and stock trading are only some examples of\nthese daily-increasing applications.<\/jats:p>\n               <jats:p>Since the growth of data <jats:italic>volumes<\/jats:italic> is accompanied by a similar raise in their dimensionalities, clusters cannot be\nexpected to completely appear when considering all attributes together. Subspace clustering is a general approach that\nsolved that issue by automatically finding the hidden clusters within different subsets of the attributes rather than\nconsidering all attributes together.<\/jats:p>\n               <jats:p>In this article, novel methods for an efficient subspace clustering of high-dimensional big data streams are\npresented. Approaches that efficiently combine the anytime clustering concept with the stream subspace clustering paradigm\nare discussed. Additionally, efficient and adaptive density-based clustering algorithms are presented for high-dimensional\ndata streams. Novel open-source assessment framework and evaluation measures are additionally presented for subspace\nstream clustering.<\/jats:p>","DOI":"10.1515\/itit-2016-0007","type":"journal-article","created":{"date-parts":[[2016,6,27]],"date-time":"2016-06-27T16:14:52Z","timestamp":1467044092000},"page":"206-213","source":"Crossref","is-referenced-by-count":4,"title":["Clustering Big Data streams: recent challenges and contributions"],"prefix":"10.1515","volume":"58","author":[{"given":"Marwan","family":"Hassani","sequence":"first","affiliation":[{"name":"RWTH Aachen University, Data Management and Data Exploration Group, D-52074 Aachen, Germany"}]},{"given":"Thomas","family":"Seidl","sequence":"additional","affiliation":[{"name":"Ludwig-Maximilians-Universit\u00e4t (LMU) Munich, Database Systems Group, D-80538 M\u00fcnchen, Germany"}]}],"member":"374","published-online":{"date-parts":[[2016,6,24]]},"container-title":["it - Information Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/itit.2016.58.issue-4\/itit-2016-0007\/itit-2016-0007.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0007\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0007\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T11:44:47Z","timestamp":1624448687000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0007\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,24]]},"references-count":0,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,6,24]]},"published-print":{"date-parts":[[2016,8,28]]}},"alternative-id":["10.1515\/itit-2016-0007"],"URL":"https:\/\/doi.org\/10.1515\/itit-2016-0007","relation":{},"ISSN":["1611-2776","2196-7032"],"issn-type":[{"value":"1611-2776","type":"print"},{"value":"2196-7032","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6,24]]}}}