Behavioral data from computer games can be exceptionally high-dimensional, of massive
scale and cover a temporal segment reaching years of real-time and a varying population of …

Despite the prevalence of gaming as a human activity, the literature on playtime is
uninformed by large-scale, high-quality data. This has led to an evidence-base in which the …

Climate change, the global energy footprint, and strategies for sustainable development
have become topics of considerable political and public interest. The public debate is …

Machine learning (ML) models are developed on a learning dataset covering only a small
part of the data of interest. If model predictions are accurate for the learning dataset but fail …

Non-negative matrix factorization (NMF) has become a standard tool in data mining,
information retrieval, and signal processing. It is used to factorize a non-negative data matrix …

In this paper machine learning methods for automatic classification problems using
computational geometry are considered. Classes are defined with convex hulls of points …

Behavioral datasets from major commercial game titles of the “AAA” grade generally feature
high dimensionality and large sample sizes, from tens of thousands to millions, covering …

We present an extension of convex-hull non-negative matrix factorization (CH-NMF) which
was recently proposed as a large scale variant of convex non-negative matrix factorization …

The first stage of organizing objects is to partition them into groups or clusters. The clustering
is generally done on individual object data representing the entities such as feature vectors …

Abstract Machine learning methods for automatic classification problems using
computational geometry are considered. Classes are defined by convex hulls of sets of …