Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we
study a version of constrained SC in which we try to incorporate the fairness notion …

Clustering is a well-known unsupervised machine learning approach capable of
automatically grouping discrete sets of instances with similar characteristics. Constrained …

Unsupervised co-analysis of a set of shapes is a difficult problem since the geometry of the
shapes alone cannot always fully describe the semantics of the shape parts. In this paper …

Visual analytics is the science of marrying interactive visualizations and analytic algorithms
to support exploratory knowledge discovery in large datasets. We argue for a shift from a …

Constrained clustering has been well-studied for algorithms such as K-means and
hierarchical clustering. However, how to satisfy many constraints in these algorithmic …

Spectral clustering methods are gaining more and more interests and successfully applied
in many fields because of their superior performance. However, there still exist two main …

In this article, we propose a semi-supervised non-negative matrix factorization (NMF) model
by means of elegantly modeling the label information. The proposed model is capable of …

Clustering face images according to their latent identity has two important applications: 1)
grouping a collection of face images when no external labels are associated with images …

Observational data usually comes with a multimodal nature, which means that it can be
naturally represented by a multi-layer graph whose layers share the same set of vertices …

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 …