This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately,
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …

This paper introduces time‐varying grouped patterns of heterogeneity in linear panel data
models. A distinctive feature of our approach is that group membership is left unrestricted …

Clustering is the most fundamental technique for data processing. This paper presents a
collaborative annealing power k-means++ clustering algorithm by integrating the k-means++ …

Clustering is an important Machine Learning task, which aims at discovering the implicit
structure of data. Applying a clustering algorithm is easy but since clustering is an …

Abstract Machine Learning (ML) has been categorized as a branch of Artificial Intelligence
(AI) under the Computer Science domain wherein programmable machines imitate human …

Constrained Clustering allows to make the clustering task more accurate by integrating user
constraints, which can be instance-level or cluster-level constraints. Few works consider the …

In recent decades, unmanned surface vehicles (USVs) are attracting increasing attention
due to their underlying capability in autonomously undertaking complex maritime tasks in …

Using a panel dataset of 70 countries spanning 42 years (1970–2011), we investigate the
distinct effects of social globalisation and trade openness on national trends in markers of …

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