Clustering Approach to Analyzing Student Data by using K-Means Algorithm

dc.contributor.author	Wai, Khin Su Su
dc.contributor.author	Min, Myat Myat
dc.date.accessioned	2019-07-22T04:49:12Z
dc.date.available	2019-07-22T04:49:12Z
dc.date.issued	2010-12-16
dc.identifier.uri	http://onlineresource.ucsy.edu.mm/handle/123456789/1130
dc.description.abstract	Clustering is the process of grouping data into classes of clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. K-means clustering is a partitioning method. . Kmeans clustering algorithm is used to cluster the student data. The proposed system finds the relationship between students’ government technology high school (G.T.H.S) entrance examination results and their success using cluster analysis. Euclidean distance measure also used to calculate the closest centroids for each object.	en_US
dc.language.iso	en	en_US
dc.publisher	Fifth Local Conference on Parallel and Soft Computing	en_US
dc.subject	Clustering Approach	en_US
dc.subject	K-means Algorithm	en_US
dc.subject	Euclidean distance	en_US
dc.title	Clustering Approach to Analyzing Student Data by using K-Means Algorithm	en_US
dc.type	Article	en_US