Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

The tremendous growth of event dissemination over social networks makes it very
challenging to accurately discover and track exciting events, as well as their evolution and …

Developing effective visual analytics systems demands care in characterization of domain
problems and integration of visualization techniques and computational models. Urban …

The purpose of this study is to review, synthesize and critique the voluminous,
multidisciplinary literature on factors influencing ethical decision making (EDM) by adopting …

Interactive machine learning (IML) is an iterative learning process that tightly couples a
human with a machine learner, which is widely used by researchers and practitioners to …

Visualization and artificial intelligence (AI) are well-applied approaches to data analysis. On
one hand, visualization can facilitate humans in data understanding through intuitive visual …

The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of
heterogeneous data. Thus, traditional approaches quickly became unpractical for real life …

The spatial time series generated by city sensors allow us to observe urban phenomena like
environmental pollution and traffic congestion at an unprecedented scale. However …

Time-series data is widely studied in various scenarios, like weather forecast, stock market,
customer behavior analysis. To comprehensively learn about the dynamic environments, it is …

This paper provides a short overview of space–time series clustering, which can be
generally grouped into three main categories such as: hierarchical, partitioning-based, and …