The k-means clustering algorithm with k-means++ initialization is relatively simple, easy to implement, and effective. One disadvantage of k-means clustering is that it only works with strictly ...

Because of this, k-means clustering can yield different results on different runs of the algorithm — which isn’t ideal in mission-critical domains like finance.

In this paper, the authors contain a partitional based algorithm for clustering high-dimensional objects in subspaces for iris gene dataset. In high dimensional data, clusters of objects often ...

J. A. Hartigan, M. A. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 28, No. 1 ...

The K-means algorithm is usually widely used in cluster analysis, but it is easily disturbed when dealing with data containing outliers.

A team of researchers from the University of Professional Studies, Accra, led by Dr. Augustina Dede Agor, has completed one of the most detailed global reviews to date on the integration of artificial ...