Constrained spectral embedding for K-way data clustering

Constrained spectral embedding for K-way data clustering

Constrained 1-Spectral Clustering

An important form of prior information in clustering comes in form of cannot-link and must-link constraints. We present a generalization of the popular spectral clustering technique which integrates such constraints. Motivated by the recently proposed 1-spectral clustering for the unconstrained problem, our method is based on a tight relaxation of the constrained normalized cut into a continuou...

Spectral Relaxation Models and Structure Analysis for K-way Graph Clustering and Bi-clustering

Constrained K-Means Clustering

We consider practical methods for adding constraints to the K-Means clustering algorithm in order to avoid local solutions with empty clusters or clusters having very few points. We often observe this phenomena when applying K-Means to datasets where the number of dimensions is n 10 and the number of desired clusters is k 20. We propose explicitly adding k constraints to the underlying clusteri...

Repeated Record Ordering for Constrained Size Clustering

One of the main techniques used in data mining is data clustering, which has many applications in computer science, biology, and social sciences. Constrained clustering is a type of clustering in which side information provided by the user is incorporated into current clustering algorithms. One of the well researched constrained clustering algorithms is called microaggregation. In a microaggreg...