Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of
Explainable Artificial Intelligence. In Supervised Classification, this means associating with …

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is traditionally
considered an unsupervised learning task. In recent years, the use of background …

We discuss the application of random projections to conic programming: notably linear,
second-order and semidefinite programs. We prove general approximation results on …

One way to solve very large linear programs in standard form is to apply a random projection
to the constraints, then solve the projected linear program. This will yield a guaranteed …

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, has been
recently extended to exploit prior knowledge on the cardinality of each cluster. Such …

In this article, we consider distance-based clustering problems. In contrast to many
approaches, we use the maximum norm instead of the more commonly used Euclidean …

This study explores a dynamic checkpoint strategy for an on-demand flexible transit service
called a Mobility Allowance Shuttle Transit with Dynamic Checkpoint (MAST-DC) that …

The minimum sum-of-squares clustering problem is a very important problem in data mining
and machine learning with very many applications in, eg, medicine or social sciences …