… between distance functions and clustering algorithms, while the second focuses on the use
of distance functions in … Moreover, among the plethora of distance functions available in the …

… and Manhattan distance function. Experiment and results are shown to observe the effect of
these distance function upon kmeans clustering. The distance functions are compared using …

… cluster centroids that can minimize the objective function of … k-means clustering algorithm
based on the proposed distance. … clustering performance of the functional k-means clustering …

… We have implemented the distance functions and clustering algorithms in the aeon toolkit.
Details of the implementation and guidance on reproducing results are provided in “Appendix …

… This paper first presents two distance functions, which are used to measure the difference …
-clustering. The comparison results prove that our algorithm utilizing two distance functions is …

… -to-point distances. In this research, the K-means clustering algorithm is evaluated with three
… Garg, “Effect of distance functions on k-means clustering algorithm,” International Journal of …

… of the clustering algorithm. … distance function, which will lead to improved clustering. Our
main contribution is DistBoost - a novel semi-supervised algorithm for learning distance functions…

… We implemented our approach using the k -means clustering algorithm. We evaluated our
… NCC, that uses the learnt distance function and cluster centroids instead of all the points of a …

… fuzzy clustering algorithms, takes … cluster, so as to cluster with the distance function based
on the hypersphere. MM, based on the Mountain Function, is used to find the center of cluster. …

… into disjoint clusters. Variations of K-means where the Euclidean distance function is replaced
by another distance … of the method is strongly related to the distance used [3]. Although this …