{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:26:11Z","timestamp":1740201971270,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008]]},"abstract":"<jats:p>Data mining seeks to discover novel and actionable knowledge hidden in the data. As dealing with large, noisy data is a defining characteristic for data mining, where the noise in a data source comes from, whether the noisy items are randomly generated (called random noise) or they comply with some types of generative models (called systematic noise), and how we use these data errors to boost the succeeding mining process and generate better results, are all important and challenging issues that existing data mining algorithms can not yet directly solve.<\/jats:p>","DOI":"10.3233\/978-1-58603-900-4-5","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:26:53Z","timestamp":1740133613000},"source":"Crossref","is-referenced-by-count":0,"title":["Error-Tolerant Data Mining"],"prefix":"10.3233","author":[{"family":"Wu Xindong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Knowledge-Based Software Engineering"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:28:31Z","timestamp":1740133711000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=180&spage=5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-58603-900-4-5","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2008]]}}}