Integer Programming Based Algorithms for Overlapping Correlation Clustering

Clustering is a fundamental problem in data science with diverse applications in biology. The problem has many combinatorial and statistical variants, yet few allow clusters to overlap which is common in the biological domain. Recently, Bonchi et al. defined a new variant of the clustering problem, termed overlapping correlation clustering, which calls for multi-label cluster assignments that correlate with an input similarity between elements as much as possible. This variant is NP-hard and was solved by Bonchi et al. using a local search heuristic. We revisit this heuristic and develop exact integer-programming based variants for it. We show that these variants perform well across several datasets and evaluation measures.