Tom Coleman - Academia.edu (original) (raw)

Papers by Tom Coleman

Proceedings of the Twenty-First Annual ACM- …, Jan 1, 2010

This paper introduces a polynomial time approximation scheme for the metric Correlation Clusterin... more This paper introduces a polynomial time approximation scheme for the metric Correlation Clustering problem, when the number of clusters returned is bounded (by k). Consensus Clustering is a fundamental aggregation problem, with considerable application, and it is analysed here as a metric variant of the Correlation Clustering problem. The PTAS exploits a connection between Correlation Clustering and the k-cut problems. This requires the introduction of a new rebalancing technique, based on minimum cost perfect matchings, to provide clusters of the required sizes.

A local-search 2-approximation for 2-correlation-clustering

Algorithms-ESA 2008, Jan 1, 2008

CorrelationClustering is now an established problem in the algorithms and constrained clustering ... more CorrelationClustering is now an established problem in the algorithms and constrained clustering communities. With the requirement that at most two clusters be formed, the minimisation problem is related to the study of signed graphs in the social psychology ...

Journal of Experimental Algorithmics (JEA), Jan 1, 2009

Ranking data is a fundamental organizational activity. Given advice, we may wish to rank a set of... more Ranking data is a fundamental organizational activity. Given advice, we may wish to rank a set of items to satisfy as much of that advice as possible. In the Feedback Arc Set (FAS) problem, advice takes the form of pairwise ordering statements, 'a should be ranked before b'. Instances in which there is advice about every pair of items is known as a tournament. This task is equivalent to ordering the nodes of a given directed graph to minimize the number of arcs pointing in one direction.

… of the thirteenth Australasian symposium on …, Jan 1, 2007

We study the manipulation of voting schemes, where a voter lies about their preferences in the ho... more We study the manipulation of voting schemes, where a voter lies about their preferences in the hope of improving the election's outcome. All voting schemes are potentially manipulable. However, some, such as the Single Transferable Vote (STV) scheme used in Australian elections, are resistant to manipulation because it is N P-hard to compute the manipulating vote(s). We concentrate on STV and some natural generalisations of it called Scoring Elimination Protocols. We show that the hardness result for STV is true only if both the number of voters and the number of candidates are unbounded-we provide algorithms for a manipulation if either of these is fixed. This means that manipulation would not be hard in practice when either number is small. Next we show that the weighted version of the manipulation problem is N P-hard for all Scoring Elimination Protocols except one, which we provide an algorithm for manipulating. Finally we experimentally test a heuristic for solving the manipulation problem and conclude that it would not usually be effective.

Proceedings of the Twenty-First Annual ACM- …, Jan 1, 2010

This paper introduces a polynomial time approximation scheme for the metric Correlation Clusterin... more This paper introduces a polynomial time approximation scheme for the metric Correlation Clustering problem, when the number of clusters returned is bounded (by k). Consensus Clustering is a fundamental aggregation problem, with considerable application, and it is analysed here as a metric variant of the Correlation Clustering problem. The PTAS exploits a connection between Correlation Clustering and the k-cut problems. This requires the introduction of a new rebalancing technique, based on minimum cost perfect matchings, to provide clusters of the required sizes.

A local-search 2-approximation for 2-correlation-clustering

Algorithms-ESA 2008, Jan 1, 2008

CorrelationClustering is now an established problem in the algorithms and constrained clustering ... more CorrelationClustering is now an established problem in the algorithms and constrained clustering communities. With the requirement that at most two clusters be formed, the minimisation problem is related to the study of signed graphs in the social psychology ...

Journal of Experimental Algorithmics (JEA), Jan 1, 2009

Ranking data is a fundamental organizational activity. Given advice, we may wish to rank a set of... more Ranking data is a fundamental organizational activity. Given advice, we may wish to rank a set of items to satisfy as much of that advice as possible. In the Feedback Arc Set (FAS) problem, advice takes the form of pairwise ordering statements, 'a should be ranked before b'. Instances in which there is advice about every pair of items is known as a tournament. This task is equivalent to ordering the nodes of a given directed graph to minimize the number of arcs pointing in one direction.

… of the thirteenth Australasian symposium on …, Jan 1, 2007

We study the manipulation of voting schemes, where a voter lies about their preferences in the ho... more We study the manipulation of voting schemes, where a voter lies about their preferences in the hope of improving the election's outcome. All voting schemes are potentially manipulable. However, some, such as the Single Transferable Vote (STV) scheme used in Australian elections, are resistant to manipulation because it is N P-hard to compute the manipulating vote(s). We concentrate on STV and some natural generalisations of it called Scoring Elimination Protocols. We show that the hardness result for STV is true only if both the number of voters and the number of candidates are unbounded-we provide algorithms for a manipulation if either of these is fixed. This means that manipulation would not be hard in practice when either number is small. Next we show that the weighted version of the manipulation problem is N P-hard for all Scoring Elimination Protocols except one, which we provide an algorithm for manipulating. Finally we experimentally test a heuristic for solving the manipulation problem and conclude that it would not usually be effective.