A common way to add background knowledge to the clustering algorithms is by adding constraints. Though there had been some algorithms that incorporate constraints into the clustering process, not much focus was given to the topic of graph-based clustering with constraints. In this paper, we propose a constrained graph-based clustering method and argue that adding constraints in distance functio...

This paper seeks to address the problem of the empirical identification of housing market segmentation, once we assume that submarkets exist. The typical difficulty in identifying housing submarkets when dealing with many locations is the vast number of potential solutions and, in such cases, the use of the Chow test for hedonic functions is not a practical solution. Here, we solve this problem...

The area of constrained clustering has been extensively explored by researchers and used practitioners. Constrained formulations exist for popular algorithms such as k-means, mixture models, spectral but have several limitations. A fundamental strength deep learning is its flexibility, here we explore a framework in particular how it can extend the field clustering. We show that our not only ha...

The crosslingual link detection problem calls for identifying news articles in multiple languages that report on the same news event. This paper presents a novel approach based on constrained clustering. We discuss a general way for constrained clustering using a recent, graph-based clustering framework called correlation clustering. We introduce a correlation clustering implementation that fea...

In the paper a view, based on the optimization approach, is given on different types of constrained clustering problems and methods for their solution.