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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2018

30.11.2017

Density Based Cluster Growing via Dominant Sets

verfasst von: Jian Hou, Xu E, Weixue Liu

Erschienen in: Neural Processing Letters | Ausgabe 2/2018

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Abstract

Although there are a lot of clustering algorithms available in the literature, existing algorithms are usually afflicted by practical problems of one form or another, including parameter dependence and the inability to generate clusters of arbitrary shapes. In this paper we aim to solve these two problems by merging the merits of dominant sets and density based clustering algorithms. We firstly apply histogram equalization to eliminate the parameter dependence problem of the dominant sets algorithm. Noticing that the obtained clusters are usually smaller than the real ones, a density threshold based cluster growing step is then used to improve the clustering results, where the involved parameters are determined based on the initial clusters. This is followed by the second cluster growing step which makes use of the density relationship between neighboring data. Data clustering experiments and comparison with other algorithms validate the effectiveness of the proposed algorithm.
Vorheriger Artikel Soft-Constrained Neural Networks for Nonparametric Density Estimation
Nächster Artikel A New Conjugate Gradient Method with Smoothing Regularization Based on a Modified Secant Equation for Training Neural Networks

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Metadaten
Titel
Density Based Cluster Growing via Dominant Sets
verfasst von
Jian Hou
Xu E
Weixue Liu
Publikationsdatum
30.11.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9767-3

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