Parameter selection algorithm of DBSCAN based on K-means two classification algorithm

Abstract

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.

Keywords

• data analysis
• unsupervised learning
• pattern clustering
• pattern classification
• human factors
• start critical value clustering
• dbscan algorithm
• neighbourhood radius
• experimental data analysis show
• minimum point number
• maximum distance value
• cluster centre-to-centre distance
• data points
• clustering centres
• noise density clustering algorithm
• unsupervised learning
• classification algorithm
• parameter selection algorithm