The k-means is the most popular clustering algorithm, but, as it needs too many distance
computations, its speed is dramatically fall down against high-dimensional data. Although …

Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid
selection and perform poorly on non-convex dataset. To address these problems, a novel …

Multi-view subspace clustering achieves impressive performance for high-dimensional data.
However, many of these models do not sufficiently mine the intrinsic information among …

The link with exponential families has allowed k-means clustering to be generalized to a
wide variety of data-generating distributions in exponential families and clustering …

Recent progress in center-based clustering algorithms combats poor local minima by implicit
annealing through a family of generalized means. These methods are variations of Lloyd's …

Principal component analysis (PCA) is a fundamental tool for data visualization, denoising,
and dimensionality reduction. It is widely popular in statistics, machine learning, computer …

We propose a general approach for distance based clustering, using the gradient of the cost
function that measures clustering quality with respect to cluster assignments and cluster …

We prove asymptotic convergence for a general class of k-means algorithms performed over
streaming data from a distribution–the centers asymptotically converge to the set of …

We develop a family of distributed center-based clustering algorithms that work over
connected networks of users. In the proposed scenario, users contain a local dataset and …

Clustering complex high-dimensional data is particularly challenging as the signal-to-noise
ratio in such data is significantly lower than their classical counterparts. This is mainly …