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Clustering Patient Records using Fuzzy C-Means and Hard C-Means Algorithm

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dc.contributor.author Soe, Theint Theint Nwe
dc.contributor.author Htwe, Tin Tin
dc.date.accessioned 2019-07-12T03:59:51Z
dc.date.available 2019-07-12T03:59:51Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/812
dc.description.abstract Data clustering is the process of dividing data elements into classes or clusters so that items in the same class are as similar as possible, and items in different classes are as dissimilar as possible. Most clustering algorithms, assign each data to exactly one cluster, thus forming a crisp (hard) partition of the given data, but fuzzy (soft) partition allows for degrees of membership, to which data belongs to different clusters. In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. In fuzzy clustering, data elements can belong to more than one cluster, and associated with each element is a set of assigning these membership levels, and then using them to assign data elements to one or more cluster. This system is implemented clustering data by using Fuzzy C-Means (FCM) and Hard C-Means (HCM) clustering algorithms. en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.title Clustering Patient Records using Fuzzy C-Means and Hard C-Means Algorithm en_US
dc.type Article en_US


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