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 …

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

Constrained clustering has been well-studied for algorithms like K-means and hierarchical
agglomerative clustering. However, how to encode constraints into spectral clustering …

We propose a novel framework for constrained spectral clustering with pairwise constraints
which specify whether two objects belong to the same cluster or not. Unlike previous …

Many modern clustering models can be divided into two separated steps, ie, constructing a
similarity graph (SG) upon samples and partitioning each sample into the corresponding …

An important form of prior information in clustering comes in the form of cannot-link and must-
link constraints of instances. We present a generalization of the popular spectral clustering …

The technique of spectral clustering is widely used to segment a range of data from graphs
to images. Our work marks a natural progression of spectral clustering from the original …

The aim of data mining is to find novel and actionable insights in data. However, most
algorithms typically just find a single (possibly non-novel/actionable) interpretation of the …

The area of clustering under constraints has recently received much attention in the data
mining community. However, most work involves adding constraints to existing algorithms …

Constrained clustering uses pre-given knowledge to improve the clustering performance.
Here we use a new constraint called partition level side information and propose the …