<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the <italic>eps</italic> radius and the <italic>MinPts</italic> density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its <italic>k</italic>-th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.</p></abstract>

Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the eps radius and the MinPts density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k-th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.

A New Method for Automatic Determining of the DBSCAN Parameters

School of Marine Electrical Engineering Dalian Maritime University

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"A New Method for Automatic Determining of the DBSCAN Parameters"}

Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The...

A New Method for Automatic Determining of the DBSCAN Parameters

Publié en ligne: 23 mai 2020

Pages: 209 - 221

Reçu: 10 août 2019

Accepté: 03 mars 2020

DOI: https://doi.org/10.2478/jaiscr-2020-0014

Mots clés
clustering algorithms, DBSCAN, data mining

© 2020 Artur Starczewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A New Method for Automatic Determining of the DBSCAN Parameters

Publié en ligne: 23 mai 2020

Pages: 209 - 221

Reçu: 10 août 2019

Accepté: 03 mars 2020

DOI: https://doi.org/10.2478/jaiscr-2020-0014

Mots clésclustering algorithms, DBSCAN, data mining

© 2020 Artur Starczewski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
clustering algorithms, DBSCAN, data mining