Consensus Clustering Algorithms: Comparison and Refinement

Consensus clustering is the problem of reconciling clustering information about the same data set coming from different sources or from different runs of the same algorithm. Cast as an optimization problem, consensus clustering is known as median partition, and has been shown to be NP-complete. A number of heuristics have been proposed as approximate solutions, some with performance guarantees. In practice, the problem is apparently easy to approximate, but guidance is necessary as to which heuristic to use depending on the number of elements and clusterings given. We have implemented a number of heuristics for the consensus clustering problem, and here we compare their performance, independent of data size, in terms of efficacy and efficiency, on both simulated and real data sets. We find that based on the underlying algorithms and their behavior in practice the heuristics can be categorized into two distinct groups, with ramification as to which one to use in a given situation, and ...

• Type: Conference
• Year: 2008
• Where: ALENEX
• Authors: Andrey Goder, Vladimir Filkov
• Keywords: ALENEX 2008, Algorithms, Consensus Clustering, Consensus Clustering Problem, Heuristic