Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4808
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorTing, SL-
dc.creatorShum, CC-
dc.creatorKwok, SK-
dc.creatorTsang, AHC-
dc.creatorLee, WB-
dc.date.accessioned2014-12-11T08:28:56Z-
dc.date.available2014-12-11T08:28:56Z-
dc.identifier.issn1945-3116 (print)-
dc.identifier.issn1945-3124 (online)-
dc.identifier.urihttp://hdl.handle.net/10397/4808-
dc.language.isoenen_US
dc.publisherScientific Research Publishingen_US
dc.rightsCopyright © 2009 SciResen_US
dc.rightsJournal of software engineering and applications is available online at: http://www.scirp.org/journal/jsea. This is a postprint of an article. The definitive, peer-reviewed and edited version of this article is published in Journal of software engineering and applications, v. 2, no. 3, p. 150-159, doi: 10.4236/jsea.2009.23022.en_US
dc.subjectData miningen_US
dc.subjectBiomedicineen_US
dc.titleData mining in biomedicine : current applications and further directions for researchen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this manuscript: S. K. Kwoken_US
dc.description.otherinformationAuthor name used in this manuscript: A. H. C. Tsangen_US
dc.identifier.spage150-
dc.identifier.epage159-
dc.identifier.volume2-
dc.identifier.issue3-
dc.identifier.doi10.4236/jsea.2009.23022-
dcterms.abstractData mining is the process of finding the patterns, associations or relationships among data using different analytical techniques involving the creation of a model and the concluded result will become useful information or knowledge. The advancement of the new medical deceives and the database management systems create a huge number of data-bases in the biomedicine world. Establishing a methodology for knowledge discovery and management of the large amounts of heterogeneous data has become a major priority of research. This paper introduces some basic data mining techniques, unsupervised learning and supervising learning, and reviews the application of data mining in biomedicine. Applications of the multimedia mining, including text, image, video and web mining are discussed. The key issues faced by the computing professional, medical doctors and clinicians are highlighted. We also state some foreseeable future developments in the field. Although extracting useful information from raw biomedical data is a challenging task, data mining is still a good area of scientific study and remains a promising and rich field for research.-
dcterms.bibliographicCitationJournal of software engineering and applications, Oct. 2009, v. 2, no. 3, p. 150-159-
dcterms.isPartOfJournal of software engineering and applications-
dcterms.issued2009-10-
dc.identifier.rosgroupidr49928-
dc.description.ros2009-2010 > Academic research: refereed > Publication in refereed journal-
dc.description.oapreprint_postprint-
Appears in Collections:Journal/Magazine Article
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