In many machine learning applications, labeled data is scarce and obtaining more labels is
expensive. We introduce a new approach to supervising neural networks by specifying …

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 …

Clustering is an unsupervised process which aims to discover regularities and underlying
structures in data. Constrained clustering extends clustering in such a way that expert …

Constrained clustering is becoming an increasingly popular approach in data mining. It
offers a balance between the complexity of producing a formal definition of thematic classes …

Identifying important components or factors in large amounts of noisy data is a key problem
in machine learning and data mining. Motivated by a pattern decomposition problem in …

Abstract Machine learning (ML) models have been quite successful in predicting outcomes
in many applications. However, in some cases, domain experts might have a judgment …

This article proposes a hyperparameter optimization method for density-based spatial
clustering of applications with noise (DBSCAN) with constraints, termed HC-DBSCAN. While …

Incorporating background knowledge in clustering problems has attracted wide interest. This
knowledge can be represented as pairwise instance-level constraints. Existing techniques …

Clustering is a challenging problem because given the same data set, it can be grouped in
multiple different ways. Which of these clustering solutions is interesting depends on its …

A keyword search on constrained clustering on Web-of-Science returned just under 3,000
documents. We ran automatic analyses of those, and compiled our own bibliography of 183 …