… Different distance metrics are implemented to measure the distance between data objects.
In this paper, the K-means algorithm is employed by using different distance metrics, whose …

… distance metric plays an important role in clustering and there are various metric functions
are available for clustering… clustering using Euclidean distance and Manhattan distance is …

… of well-known clustering algorithms based on ten different distance measures. The results
… performance evaluation metrics such as accuracy, inter-cluster and intra-cluster distance [4]. …

… This distance metric plays a very important role in clustering techniques. … for clustering. In
the current paper, the solution of k-means clustering algorithm using Manhattan distance metric …

… 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 …

… A new evolving clustering algorithm using the proposed distance is also proposed in this
paper. Numerical examples with benchmark datasets reveal that the direction-aware …

… We evaluate clustering algorithms and cluster quality metrics on graphs ranging … metrics
and information recovery metrics, with conductance as the best of the stand-alone quality metrics…

… distance metrics. In order to find out the possibility of different … The algorithm is executed on
6 different variations of the IRIS … algorithm for each distinct pair of data and distance metrics. …

… new clustering algorithms for metric and nonmetric distance … To obtain new clustering
algorithms for specific dissimilarity … Itakura-Saito divergence, Mahalanobis distances, etc., we …

… , we propose a new distance metric for the K-means clustering algorithm. Applying … metric
in clustering a dataset, forms unequal clusters. This metric leads to a larger size for a cluster …