… the distance between data objects. In this paper, the K-means algorithm is employed by
using different distance metrics, whose mechanisms are summarized as follows. …

… outperforms the k-means clustering using Manhattan distance in terms of number of iteration…
different clustering algorithm for other different distance metric can be figured out using other …

… k-means algorithm using three different metrics Euclidean, Manhattan and Minkowski
distance metrics along with the comparative study of results of basic k-means algorithm which is …

… So, it is important to find set of distance measures for different clustering techniques on …
different distance measures on eight clustering techniques. The quality of the distance measures …

… metric, and a comprehensive comparison of the usage of other distance metrics in k-means
algorithm is lacking. The primary focus of this paper is on such a comparison. …

… algorithm, an empirical evaluation has been performed. In this paper, accuracy, … 13 different
distance/similarity measures over 6 different variations of data using k-means algorithm have …

… the same clustering algorithm, more exactly Agglomerative clustering, but regarding the
metric distance, we use both metrics: Euclidean distance and Manhattan distance. They give us …

… measure both the information recovery of each clustering and the quality of each clustering
with various metrics… Then, we test the performance of the clustering algorithms on real-world …

… number of new clustering algorithms for metric and nonmetric distance measures. Stated in
… like the Kullback-Leibler divergence, Itakura-Saito divergence, Mahalanobis distances, etc., …

… K-Means algorithm and different distance metrics used in this paper. It also provides the basics
of the three evaluation measures … In Section III, the proposed distance metric is presented …