The k-means clustering algorithm, whilst widely popular, is not without its drawbacks. In this
paper, we focus on the sensitivity of k-means to its initial set of centroids. Since the cluster …

K-Means is one of the most used algorithms for data clustering and the usual clustering
method for benchmarking. Despite its wide application it is well-known that it suffers from a …

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately,
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …

The quality of clustering results obtained with the k-means algorithm depends heavily on the
initialization of the cluster centers. Simply sampling centers uniformly at random from the …

Clustering is an exploratory data analysis tool that has gained enormous attention in the
recent years specifically for gene expression data analysis. The K-means clustering is a …

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately,
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …

Clustering is one of the most important tasks in exploratory data analysis. One of the
simplest and the most widely used clustering algorithm is K-means which was proposed in …

Over the past five decades, k-means has become the clustering algorithm of choice in many
application domains primarily due to its simplicity, time/space efficiency, and invariance to …

The k-means algorithm is widely used in various research fields because of its fast
convergence to the cost function minima; however, it frequently gets stuck in local optima as …

One of the greatest challenges in k-means clustering is positioning the initial cluster centers,
or centroids, as close to optimal as possible, and doing so in an amount of time deemed …