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 …

Multiple kernel clustering (MKC) algorithm aims to group data into different categories by
optimally integrating information from a group of pre-specified kernels. Though …

Random swap algorithm aims at solving clustering by a sequence of prototype swaps, and
by fine-tuning their exact location by k-means. This randomized search strategy is simple to …

Abstract Gaussian Mixture Model (GMM) is a popular clustering algorithm due to its neat
statistical properties, which enable the “soft” clustering and the determination of the number …

Internal cluster validity measures (such as the Calinski–Harabasz, Dunn, or Davies–Bouldin
indices) are frequently used for selecting the appropriate number of partitions a dataset …

The scalability of traveling salesperson problem (TSP) algorithms for handling large-scale
problem instances has been an open problem for a long time. We arranged a so-called …

Three-way clustering provides an effective framework for clustering of data in the presence
of uncertain, imprecise and incomplete data. In this article, we used ideas inspired from two …

Border-Peeling algorithm is a recently proposed density based clustering algorithm. The
method of peeling off border points by continuous iteration and calculating the density …

We derive a new Bayesian Information Criterion (BIC) by formulating the problem of
estimating the number of clusters in an observed dataset as maximization of the posterior …

We hypothesize that similar objects should have similar outlier scores. To the best of our
knowledge, all existing outlier detectors calculate the outlier score for each object …