Under the vision of industry 4.0, the integration of drones in last-mile delivery can transform
traditional delivery practices and provide competitive advantages. However, the …

Clustering addresses the problem of assigning similar objects to groups. Since the size of
the clusters is often constrained in practical clustering applications, various capacitated …

Constrained clustering, such as k-means with instance-level Must-Link (ML) and Cannot-
Link (CL) auxiliary information as the constraints, has been extensively studied recently, due …

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 …

Witnessing the tremendous development of machine learning technology, emerging
machine learning applications impose challenges of using domain knowledge to improve …

We study here the semisupervised k-clustering problem where information is available on
whether pairs of objects are in the same or different clusters. This information is available …

The k-means algorithm is one of the most widely-used algorithms in clustering. It is known to
be effective when the clusters are homogeneous and well separated in the feature space …

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 …

The design of supply networks that are resilient to disruptions has recently attracted
considerable attention. We consider supply networks where a set of clients are served from …

In this paper, we deal with semi-supervised Minimum Sum-of-Squares Clustering (MSSC)
problems where background knowledge is given in the form of instance-level constraints. In …