When clustering a dataset, the right number k of clusters to use is often not obvious, and
choosing k automatically is a hard algorithmic problem. In this paper we present an …

The K-means clustering algorithm is well-known for its easy computational approach. In this
algorithm, essential cluster-level information is captured by the K cluster centroids. However …

The K-means algorithm is a popular approach to finding clusters due to its simplicity of
implementation and fast execution. It appears extensively in the machine learning literature …

Clustering validation is a long standing challenge in the clustering literature. While many
validation measures have been developed for evaluating the performance of clustering …

This paper has two contributions. First, we introduce a clustering basic benchmark. Second,
we study the performance of k-means using this benchmark. Specifically, we measure how …

$ k $-means clustering is a well-studied problem due to its wide applicability. Unfortunately,
there exist strong theoretical limits on the performance of any algorithm for the $ k $-means …

We present a method for initialising the K-means clustering algorithm. Our method hinges on
the use of a kd-tree to perform a density estimation of the data at various locations. We then …

K-means is a widely used partitional clustering method. While there are considerable
research efforts to characterize the key features of K-means clustering, further investigation …

The issue of determining “the right number of clusters” in K-Means has attracted
considerable interest, especially in the recent years. Cluster intermix appears to be a factor …

The performance of K-means clustering depends on the initial guess of partition. We
motivate theoretically and experimentally the use of a deterministic divisive hierarchical …