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Clustering Algorithms using Different Distance
Abstract
Data mining istheprocessofdiscoveringmeaningfulcorrelations, trend and interesting patterns from a large volume ofdata.Clustering is the process of grouping similar data elements together. Inthis paper k-means algorithm and k-medoid algorithm is used alongwith distance measures like Euclidean, Manhattan and Squared on areal time medical data set a to group of similar patients based on theirvision ailments. The Results are compared numerically andgraphically to find the best distance measure. Experimental resultsshows that k-medoids clustering algorithm outperforms k-meansclustering. The experiment was repeated using different distancemeasures likeEuclidean, Manhatten and Squared. The results showsthat k-medoid with Euclidean distance measure forms the most densedcluster and thus it is very effective than other distance measures.
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