Spectral clustering using the kNN-MST similarity graph

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Abstract

Spectral clustering is a technique that uses the spectrum of a similarity graph to cluster data. Part of this procedure involves calculating the similarity between data points and creating a similarity graph from the resulting similarity matrix. This is ordinarily achieved by creating a k-nearest neighbour (kNN) graph. In this paper, we show the benefits of using a different similarity graph, namely the union of the kNN graph and the minimum spanning tree of the negated similarity matrix (kNN-MST). We show that this has some distinct advantages on both synthetic and real datasets. Specifically, the clustering accuracy of kNN-MST is less dependent on the choice of k than kNN is.

Original languageEnglish
Title of host publication2016 8th Computer Science and Electronic Engineering Conference, CEEC 2016 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages222-227
Number of pages6
ISBN (Print)9781509020508
DOIs
Publication statusPublished - 27 Jan 2017
Event8th Computer Science and Electronic Engineering Conference, CEEC 2016 - Colchester, United Kingdom
Duration: 28 Sept 201630 Sept 2016

Conference

Conference8th Computer Science and Electronic Engineering Conference, CEEC 2016
Country/TerritoryUnited Kingdom
CityColchester
Period28/09/201630/09/2016

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