{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T11:55:45Z","timestamp":1762430145780},"reference-count":4,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Info. Tech. Dec. Mak."],"published-print":{"date-parts":[[2006,12]]},"abstract":"<jats:p> Data stream mining has attracted considerable attention over the past few years owing to the significance of its applications. Streaming data is often evolving over time. Capturing changes could be used for detecting an event or a phenomenon in various applications. Weather conditions, economical changes, astronomical, and scientific phenomena are among a wide range of applications. Because of the high volume and speed of data streams, it is computationally hard to capture these changes from raw data in real-time. In this paper, we propose a novel algorithm that we term as STREAM-DETECT to capture these changes in data stream distribution and\/or domain using clustering result deviation. STREAM-DETECT is followed by a process of offline classification CHANGE-CLASS. This classification is concerned with the association of the history of change characteristics with the observed event or phenomenon. Experimental results show the efficiency of the proposed framework in both detecting the changes and classification accuracy. <\/jats:p>","DOI":"10.1142\/s0219622006002179","type":"journal-article","created":{"date-parts":[[2006,12,12]],"date-time":"2006-12-12T12:59:30Z","timestamp":1165928370000},"page":"659-670","source":"Crossref","is-referenced-by-count":28,"title":["DETECTION AND CLASSIFICATION OF CHANGES IN EVOLVING DATA STREAMS"],"prefix":"10.1142","volume":"05","author":[{"given":"MOHAMED MEDHAT","family":"GABER","sequence":"first","affiliation":[{"name":"School of Information Technologies, University of Sydney, NSW 2006, Australia"}]},{"given":"PHILIP S.","family":"YU","sequence":"additional","affiliation":[{"name":"IBM Thomas J. Watson Research Center, 19, Skyline Drive, Hawthorne, NY 10532, USA"}]}],"member":"219","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"rf3","volume":"34","author":"Gaber M. M.","journal-title":"ACM SIGMOD Record"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1109\/MP.2003.1197877"},{"key":"rf14","first-page":"281","volume":"8","author":"Klinkenberg R.","journal-title":"Incremental Learning Systems Capable of Dealing with Concept Drift"},{"key":"rf16","volume-title":"Advanced Methods of Knowledge Discovery from Complex Data","author":"Gaber M. M.","year":"2005"}],"container-title":["International Journal of Information Technology &amp; Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0219622006002179","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T14:20:53Z","timestamp":1565187653000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0219622006002179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,12]]},"references-count":4,"journal-issue":{"issue":"04","published-online":{"date-parts":[[2011,11,20]]},"published-print":{"date-parts":[[2006,12]]}},"alternative-id":["10.1142\/S0219622006002179"],"URL":"https:\/\/doi.org\/10.1142\/s0219622006002179","relation":{},"ISSN":["0219-6220","1793-6845"],"issn-type":[{"value":"0219-6220","type":"print"},{"value":"1793-6845","type":"electronic"}],"subject":[],"published":{"date-parts":[[2006,12]]}}}