Error bounds for correlation clustering

This paper presents a learning theoretical analysis of correlation clustering (Bansal et al., 2002). In particular, we give bounds on the error with which correlation clustering recovers the correct partition in a planted partition model (Condon & Karp, 2001; McSherry, 2001). Using these bounds, we analyze how the accuracy of correlation clustering scales with the number of clusters and the sparsity of the graph. We also propose a statistical test that analyzes the significance of the clustering found by correlation clustering.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Thorsten Joachims, John E. Hopcroft
• Keywords: Correlation Clustering, Correlation Clustering Scales, ICML 2005, Machine Learning, Planted Partition Model