ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities
Machine Learning
2019-02-06 v3 Computer Vision and Pattern Recognition
Machine Learning
Abstract
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced performances for clusters with different densities. Therefore, in this paper, an adaptive DBSCAN is proposed which can work significantly well for identifying clusters with varying densities.
Keywords
Cite
@article{arxiv.1809.06189,
title = {ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities},
author = {Mohammad Mahmudur Rahman Khan and Md. Abu Bakr Siddique and Rezoana Bente Arif and Mahjabin Rahman Oishe},
journal= {arXiv preprint arXiv:1809.06189},
year = {2019}
}
Comments
To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018)