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      AutoSCAN: automatic detection of DBSCAN parameters and efficient clustering of data in overlapping density regions

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          Abstract

          The density-based clustering method is considered a robust approach in unsupervised clustering technique due to its ability to identify outliers, form clusters of irregular shapes and automatically determine the number of clusters. These unique properties helped its pioneering algorithm, the Density-based Spatial Clustering on Applications with Noise (DBSCAN), become applicable in datasets where various number of clusters of different shapes and sizes could be detected without much interference from the user. However, the original algorithm exhibits limitations, especially towards its sensitivity on its user input parameters minPts and ɛ. Additionally, the algorithm assigned inconsistent cluster labels to data objects found in overlapping density regions of separate clusters, hence lowering its accuracy. To alleviate these specific problems and increase the clustering accuracy, we propose two methods that use the statistical data from a given dataset’s k-nearest neighbor density distribution in order to determine the optimal ɛ values. Our approach removes the burden on the users, and automatically detects the clusters of a given dataset. Furthermore, a method to identify the accurate border objects of separate clusters is proposed and implemented to solve the unpredictability of the original algorithm. Finally, in our experiments, we show that our efficient re-implementation of the original algorithm to automatically cluster datasets and improve the clustering quality of adjoining cluster members provides increase in clustering accuracy and faster running times when compared to earlier approaches.

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          Algorithm AS 136: A K-Means Clustering Algorithm

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            Comparing partitions

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              Scikit-learn: Machine Learning in Python

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                Author and article information

                Contributors
                Journal
                PeerJ Comput Sci
                PeerJ Comput Sci
                peerj-cs
                PeerJ Computer Science
                PeerJ Inc. (San Diego, USA )
                2376-5992
                14 March 2024
                2024
                : 10
                : e1921
                Affiliations
                [1 ]Department of Multimedia Engineering, Dongguk University , Seoul, South Korea
                [2 ]Department of Artificial Intelligence, Dongguk University , Seoul, South Korea
                [3 ]Division of AI Software Convergence, Dongguk University , Seoul, South Korea
                Article
                cs-1921
                10.7717/peerj-cs.1921
                11042006
                38660211
                f3d19a27-eb5a-4f57-9410-10cbb6408255
                ©2024 Bushra et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                History
                : 24 October 2023
                : 12 February 2024
                Funding
                Funded by: The National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT)
                Award ID: NRF-2022R1F1A1074228
                Funded by: Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development
                Award ID: IITP-2023-RS-2023-00254592
                Funded by: The Korean government (MSIT) and the Dongguk University Research Fund of 2023
                This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2022R1F1A1074228), and was also supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00254592) grant funded by the Korean government (MSIT) and the Dongguk University Research Fund of 2023. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Data Mining and Machine Learning
                Data Science

                dbscan,density-based clustering,unsupervised clustering,k-nearest neighbors

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