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Erschienen in:
Learning Convolutional Ranking-Score Function by Query Preference Regularization Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

An Improved Density Peak Clustering Algorithm

verfasst von : Jian Hou, Xu E

Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2017

Verlag: Springer International Publishing

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Abstract

Density based clustering is an important clustering approach due to its ability to generate clusters of arbitrary shapes. Among density based clustering algorithms, the density peak (DP) based algorithm is shown to a potential one with some attractive properties. The DP algorithm calculates the local density of each data, and then the distance of each data to its nearest neighbor with higher density. Based on these two measurements, the cluster centers can be isolated from the non-center data. As a result, the cluster centers can be identified relatively easily and the non-center data can be grouped into clusters efficiently. In this paper we study the influence of density kernels on the clustering results and present a new kernel. We also present a new cluster center selection criterion based on distance normalization. Our new algorithm is shown to be effective in experiments on ten datasets.

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Vorheriges Kapitel High-Accuracy Deep Convolution Neural Network for Image Super-Resolution
Nächstes Kapitel Consensus-based Parallel Algorithm for Robust Convex Optimization with Scenario Approach in Colored Network
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Metadaten
Titel
An Improved Density Peak Clustering Algorithm
verfasst von
Jian Hou
Xu E
Copyright-Jahr
2017
Buch
Intelligent Data Engineering and Automated Learning – IDEAL 2017

Print ISBN: 978-3-319-68934-0

Electronic ISBN: 978-3-319-68935-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-68935-7_24

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