Challenges and possible solutions to density based clustering

Clustering is an interdisciplinary-studied subject of statistical data analysis. In this study, among various types of clustering algorithms, the algorithms derived from Density Based Spatial Clustering of Applications with Noise (DBSCAN) are investigated. Although DBSCAN is the well-known density-b...

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Bibliographic Details
Published in2016 IEEE 8th International Conference on Intelligent Systems (IS) pp. 492 - 498
Main Authors Yasar, Fatma Gunseli, Ulutagay, Gozde
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Active-DBSCAN
Algorithm design and analysis
Approximation algorithms
Classification algorithms
clustering
Clustering algorithms
DBSCAN
DBSCAN-GM
density based clustering
DMDBSCAN
EDBSCAN
Fast-DBSCAN
FN-DBSCAN
LDBSCAN
Partitioning algorithms
Parzen-Window
Robustness
Soft DBSCAN
Time complexity
VDBSCAN
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DOI10.1109/IS.2016.7737466

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Summary:Clustering is an interdisciplinary-studied subject of statistical data analysis. In this study, among various types of clustering algorithms, the algorithms derived from Density Based Spatial Clustering of Applications with Noise (DBSCAN) are investigated. Although DBSCAN is the well-known density-based algorithms it has some bottlenecks. So, enhanced versions of DBSCAN are developed to provide some solutions and to ameliorate the algorithm. In this study, we provide a compact source of DBSCAN-based algorithms for the mentioned challenges.
DOI:10.1109/IS.2016.7737466