Hybrid Clustering Algorithm Based on KNN and MCL

Yan Chen; Qin Zhou Niu

doi:10.4028/www.scientific.net/AMM.610.302

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 610Hybrid Clustering Algorithm Based on KNN and MCL

Hybrid Clustering Algorithm Based on KNN and MCL

Abstract:

MCL is a graph clustering algorithm. With the characteristics of the MCL computational process, MCL is prone to producing small clustering and separating edge nodes from the group. A hybrid clustering based on MCL combined with KNN algorithm is proposed. Hybrid algorithm improves the quality of clustering by reclassification of elements in small clustering by using KNN classification characteristics and Clustering tables required by MCL clustering. Experiment proves the improved algorithm can enhance the quality of clustering.

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Periodical:

Applied Mechanics and Materials (Volume 610)

Pages:

302-306

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.610.302

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Online since:

August 2014

Authors:

Yan Chen*, Qin Zhou Niu

Keywords:

Clustering, KNN, MCL, Reclassification, Small Clustering

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References

[1] Stijn van Dongen. 2000. Performance criteria for graph clustering and markov cluster experiments. Technical report, CWI(Centre for Mathematics and Computer Science), Amsterdam, The Netherlands, The Netherlands.

Google Scholar

[2] Hiroyuki Akama, Maki Miyake, Jaeyoung Jung. How to Take Advantage of the Limitations with Markov Clustering?The Foundations of Branching Markov Clustering(BMCL).

Google Scholar

[3] UCI. UCI machine learning databases repository[D / OL]. University of California-Irvine[2010-05]. ftp: / / ftp: Ics. edu / pub / machine-learning-databases.

Google Scholar

[4] Newman M E J, Girvan M . Finding and evaluating community structrue in networks[J]. Physical Review E, 2004, 69(02).

Google Scholar