Existing deep learning methods for clustering high-dimensional data perform feature
selection and clustering separately, which can result in the exclusion of some important …

Clustering is a difficult and widely studied data mining task, with many varieties of clustering
algorithms proposed in the literature. Nearly all algorithms use a similarity measure such as …

Recent technical advances have facilitated the massive acquisition of data described by a
large number of measurable properties (high dimensional datasets). New technologies have …

Abstract Interpretation of microarray data is often crucial in various aspects of computational
analysis. Dozens of techniques like clustering, subspace-clustering are often applied to …

Massive volumes of high-dimensional data that evolve over time are continuously collected
by contemporary information processing systems, which bring up the problem of organizing …

Unsupervised learning is a fundamental category of machine learning that works on data for
which no pre-existing labels are available. Unlike in supervised learning, which has such …