Are approximation algorithms for consensus clustering worthwhile?

Bertolacci, Michael; Wirth, Anthony

doi:10.1137/1.9781611972771.41

Cited by 19 publications

(19 citation statements)

References 13 publications

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“…A number of algorithms have been published for approximating the median partition problem [3,9]. Various theoretical results, including performance guarantees have been derived, although the gap between the performance of these heuristics and their upper bounds is wide [3,9,10].…”

Section: Prior Workmentioning

confidence: 99%

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Consensus Clustering Algorithms: Comparison and Refinement

Goder

Filkov

2008

2008 Proceedings of the Tenth Workshop on Algorithm Engineering and Experiments (ALENEX)

126

103

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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 that a hybrid solution is the best bet in general. We have also developed a refined consensus clustering heuristic for the occasions when the given clusterings may be too disparate, and their consensus may not be representative of any one of them, and we show that in practice the refined consensus clusterings can be much superior to the general consensus clustering.

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Section: Prior Workmentioning

confidence: 99%

“…Various theoretical results, including performance guarantees have been derived, although the gap between the performance of these heuristics and their upper bounds is wide [3,9,10].…”

Section: Prior Workmentioning

confidence: 99%

Consensus Clustering Algorithms: Comparison and Refinement

Goder

Filkov

2008

2008 Proceedings of the Tenth Workshop on Algorithm Engineering and Experiments (ALENEX)

126

103

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“…The goal is to find a median partition for a given set of partitions, which all are over the same base set. Due to its practical relevance, Consensus Clustering has been intensively studied in terms of the usefulness of various heuristics and accompanying experiments [4,13]. The problem is defined as follows.…”

Section: According Tomentioning

confidence: 99%

“…First, we show the fixedparameter tractability of the NP-hard Consensus Clustering problem (see, e.g., [2,6,17]). Roughly speaking, the goal here is to find a median partition for a given set of partitions all over the same base set; this is motivated by the often occurring task to reconcile clustering information [4,13,17]. It is plausible that this reconciliation is only meaningful when the given input partitions have a sufficiently high degree of average similarity, because otherwise the median partition found may be meaningless since it tries to fit the demands of strongly opposing clustering proposals.…”

Section: Introductionmentioning

confidence: 99%

Average Parameterization and Partial Kernelization for Computing Medians

Betzler

Guo

Komusiewicz

et al. 2010

LATIN 2010: Theoretical Informatics

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Abstract. We propose an effective polynomial-time preprocessing strategy for intractable median problems. Developing a new methodological framework, we show that if the input instances of generally intractable problems exhibit a sufficiently high degree of similarity between each other on average, then there are efficient exact solving algorithms. In other words, we show that the median problems Swap Median Permutation, Consensus Clustering, Kemeny Score, and Kemeny Tie Score all are fixed-parameter tractable with respect to the parameter "average distance between input objects". To this end, we develop the new concept of "partial kernelization" and identify interesting polynomial-time solvable special cases for the considered problems.

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“…Finally, the CCPivot method 43,44 picks an object at random as an initial cluster pivot and then assigns to that cluster every object with a consensus similarity greater than a pre-defined threshold. It then picks another pivot from the remaining, unclustered objects and continues in this way until all the objects have been clustered (i.e., the procedure is essentially that used in the sphere exclusion approach to dissimilarity-based compound selection 45 …”

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confidence: 99%

Combining multiple classifications of chemical structures using consensus clustering

Chu

Holliday

Willett

2012

Bioorganic & Medicinal Chemistry

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Consensus clustering involves combining multiple clusterings of the same set of objects to achieve a single clustering that will, hopefully, provide a better picture of the groupings that are present in a dataset. This paper reports the use of consensus clustering methods on sets of chemical compounds represented by 2D fingerprints. Experiments with DUD, IDAlert, MDDR and MUV data suggests that consensus methods are unlikely to result in significant improvements in clustering effectiveness as compared to the use of a single clustering method.

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Are approximation algorithms for consensus clustering worthwhile?

Cited by 19 publications

References 13 publications

Consensus Clustering Algorithms: Comparison and Refinement

Consensus Clustering Algorithms: Comparison and Refinement

Average Parameterization and Partial Kernelization for Computing Medians

Combining multiple classifications of chemical structures using consensus clustering

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