2013
DOI: 10.1007/s10711-013-9848-z
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Topological graph clustering with thin position

Abstract: A clustering algorithm partitions a set of data points into smaller sets (clusters) such that each subset is more tightly packed than the whole. Many approaches to clustering translate the vector data into a graph with edges reflecting a distance or similarity metric on the points, then look for highly connected subgraphs. We introduce such an algorithm based on ideas borrowed from the topological notion of thin position for knots and 3-dimensional manifolds.

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Cited by 8 publications
(29 citation statements)
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“…The theoretical foundation for this algorithm, developed in [14], uses ideas from topology (particularly knot theory) suggesting that it should be very flexible and robust with respect to noise.…”
Section: Introductionmentioning
confidence: 99%
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“…The theoretical foundation for this algorithm, developed in [14], uses ideas from topology (particularly knot theory) suggesting that it should be very flexible and robust with respect to noise.…”
Section: Introductionmentioning
confidence: 99%
“…In [14] another topological approach to graph clustering was suggested, based on the idea of thin position for knots and 3-manifolds [9,20]. In the context of 3-manifolds, thin position determines minimal genus Heegaard splittings, which are related to minimal surfaces [19] and the Cheeger constant [15].…”
Section: Introductionmentioning
confidence: 99%
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