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Cancer Diagnosis Using K-Means Clustering

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dc.contributor.author Mon, Aye Chan
dc.date.accessioned 2019-07-31T03:49:52Z
dc.date.available 2019-07-31T03:49:52Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1488
dc.description.abstract The proliferation, ubiquity and increasing power of computer technology has aided data collection, processing, management and storage. However, the captured data needs to be converted into information and knowledge to become useful. Data mining is the process of using computing power to apply methodologies, including new techniques for knowledge discovery, to data. Data mining identifies trends within data that go beyond simple data analysis. Through the use of sophisticated algorithms, non-statistician users have the opportunity to identify key attributes of processes and target opportunities. This paper intends to support these non-statistician users in analysis of the cancer diagnosis by implementing the k-means clustering algorithm. In this paper, the Blood Cancer diagnosis is analysis in speciality. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.title Cancer Diagnosis Using K-Means Clustering en_US
dc.type Article en_US


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