Introduction: Special Issue on Theoretical Advances in Data Clustering
Clustering Large Graphs via the Singular Value Decomposition
Optimal Time Bounds for Approximate Clustering
A k-Median Algorithm with Running Time Independent of Data Size
Correlation Clustering
A New Conceptual Clustering Framework
Subquadratic Approximation Algorithms for Clustering Problems in High Dimensional Spaces
Central Clustering of Attributed Graphs
Semi-Supervised Learning on Riemannian Manifolds