Data clustering is a commonly used data processing technique in many fields, which divides
objects into different clusters in terms of some similarity measure between data points …

In this paper, leak detection and localization in water distribution networks will be reviewed.
In particular, the paper presents the evolution of the methods from model-based towards …

Hands-on Machine Learning with R provides a practical and applied approach to learning
and developing intuition into today's most popular machine learning methods. This book …

In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed
ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace …

We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc,
generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions. By …

The clustering methods have recently absorbed even-increasing attention in learning and
vision. Deep clustering combines embedding and clustering together to obtain optimal …

In this paper, we propose a recurrent framework for joint unsupervised learning of deep
representations and image clusters. In our framework, successive operations in a clustering …

Synapses are found in vast numbers in the brain and contain complex proteomes. We
developed genetic labeling and imaging methods to examine synaptic proteins in individual …

Clustering is a fundamental procedure in the analysis of scientific data. It is used
ubiquitously across the sciences. Despite decades of research, existing clustering …

Collective motions are common in crowd systems and have attracted a great deal of
attention in a variety of multidisciplinary fields. Collectiveness, which indicates the degree of …