Clustering Algorithms Using Different Distance Measures-A Comparison

Data mining is the process of discovering meaningful correlations, trend and interesting patterns from a large volume of data. Clustering is the process of grouping similar data elements together. In this paper k-means algorithm and k-medoid algorithm is used along with distance measures like Euclidean, Manhattan and Squared on areal time medical data set a to group of similar patients based on their vision ailments. The Results are compared numerically and graphically to find the best distance measure. Experimental results shows that k-medoids clustering algorithm outperforms k-means clustering. The experiment was repeated using different distance measures like Euclidean, Manhatten and Squared. The results shows that k-medoid with Euclidean distance measure forms the most densed cluster and thus it is very effective than other distance measures.