Open AccessParameter selection algorithm of DBSCAN based on K‐means two classification algorithm
Author(s) -
Chen Shouhong,
Liu Xinyu,
Ma Jun,
Zhao Shuang,
Hou Xingna
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.9082
Subject(s) - dbscan , cluster analysis , algorithm , cure data clustering algorithm , computer science , determining the number of clusters in a data set , correlation clustering , canopy clustering algorithm , k medians clustering , data stream clustering , data point , clustering high dimensional data , mathematics , data mining , artificial intelligence
Clustering algorithm is one of the most important algorithms in unsupervised learning. For density‐based spatial clustering of applications with noise (DBSCAN) density clustering algorithm, the selection of neighborhood radius and minimum number is the key to get the best clustering results. Aiming at the problems of traditional DBSCAN algorithm, such as the neighborhood radius and the minimum number of points, this article puts forward two classifications based on K‐means algorithm, and gets two clustering centers. Where calculated between two data points and the cluster center‐to ‐center distance, clustering, distance, statistics in a distance of data points within the scope of the search, the number of data points corresponding to the maximum distance value, and thus the parameters for the DBSCAN algorithm to estimate and selection of initial radius of neighborhood with the minimum number of clustering start critical value. When the parameters are iterated and optimized continuously, the data are divided into clusters, and the most suitable neighborhood radius and the minimum point number are obtained. The experimental data analysis show that the improved algorithm reduces the human factors in the traditional algorithm and improves the efficiency, so as to get the accurate clustering results.