This paper evaluates the performance efficiency of K-means clustering, Agglomerative hierarchical clustering and Density based clustering methods for low and high dimensional data. Efficiency concerns the computational time required to build up datasets. To evaluate the performance of clustering methods extensive experiments are carried out on different datasets. The results reveal that Agglomerative hierarchical clustering method is efficient in time as compared to other methods but results may vary when dataset instances are large in number.

Clustering, K-means, Agglomerative hierarchical, Euclidean

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