ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities. Khan, M. M. R., Siddique, M. A. B., Arif, R. B., & Oishe, M. R. In 2018 4th International Conference on Electrical Engineering and Information Communication Technology (iCEEiCT), pages 107–111, September, 2018. doi abstract bibtex 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.
@inproceedings{khan_adbscan_2018,
title = {{ADBSCAN}: {Adaptive} {Density}-{Based} {Spatial} {Clustering} of {Applications} with {Noise} for {Identifying} {Clusters} with {Varying} {Densities}},
shorttitle = {{ADBSCAN}},
doi = {10.1109/CEEICT.2018.8628138},
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.},
booktitle = {2018 4th {International} {Conference} on {Electrical} {Engineering} and {Information} {Communication} {Technology} ({iCEEiCT})},
author = {Khan, Mohammad Mahmudur Rahman and Siddique, Md. Abu Bakr and Arif, Rezoana Bente and Oishe, Mahjabin Rahman},
month = sep,
year = {2018},
keywords = {Adaptive DBSCAN, Artificial intelligence, Clustering algorithms, Computer science, Data mining, Data models, Databases, Flowcharts, border point, clustering algorithms, core point, data mining, density connected, density-based methods, eps, eps-neighborhood, minPts, spatial clustering},
pages = {107--111},
}
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