A Study of Clustering Techniques in Data Mining: Comparison of The K-Means and K-Medoids Clustering Algorithms

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Nowadays, thanks to advances in the field of IT, users dominate the more advanced computer Technologies and all these developments leads to the accumulation of numerical datas. Information Technologies can store accumulated datas. Data mining is used for all datas that are stored for giving meaning. Data mining contains estimation techniques or identify previously unknown meaningful techniques. Interesting patterns can be found to cluster with common features via clustering algorithms in data mining. In this study, Flags data set that taken from UCI Machine Learning Repository database, based k-means and k-medoids partitioned clustering algorithms aimed at the separation of clusters according to their country features. Application developed and presented to the users with Asp.Net in web user guide. At the end of the study, the k-means and k-medoids algorithms were checked by comparing the performances and presented suggestions for their place of use.

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Data Mining , Cluster Analysis , Asp.Net , K , Means , K , Medoids

Journal Of Natural an‎d Applied Sciences

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Journal Of Natural an‎d Applied Sciences