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AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering

Citation Author(s):
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero
Submitted by:
Pin-Yu Chen
Last updated:
5 March 2017 - 11:06pm
Document Type:
Presentation Slides
Document Year:
2017
Event:
Presenters Name:
Pin-Yu Chen
Paper Code:
1797

Abstract 

Abstract: 

One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS, an automated model order selection algorithm for SGC. Based on a recent analysis of clustering reliability for SGC under the random interconnection model, AMOS works by incrementally increasing the number of clusters, estimating the quality of identified clusters, and providing a series of clustering reliability tests. Consequently, AMOS outputs clusters of minimal model order with statistical clustering reliability guarantees. Comparing to three other automated graph clustering methods on real-world datasets, AMOS shows superior performance in terms of multiple external and internal clustering metrics. Our AMOS codes are available for download at https://github.com/tgensol/AMOS

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Dataset Files

ICASSP_AMOS_2017.pdf

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