Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications

Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including...
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Autor*in:	Darveen Vijayan [verfasserIn] Izzatdin Aziz [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	moving points HDBSCAN crowd clustering unsupervised learning

Übergeordnetes Werk:	In: Telecom - MDPI AG, 2020, 4(2022), 1, Seite 14
Übergeordnetes Werk:	volume:4 ; year:2022 ; number:1 ; pages:14

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/telecom4010001

Katalog-ID:	DOAJ087225484

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language	English
source	In Telecom 4(2022), 1, Seite 14 volume:4 year:2022 number:1 pages:14
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topic_facet	moving points HDBSCAN crowd clustering unsupervised learning Computer engineering. Computer hardware Electronic computers. Computer science
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authorswithroles_txt_mv	Darveen Vijayan @@aut@@ Izzatdin Aziz @@aut@@
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callnumber-first	T - Technology
author	Darveen Vijayan
spellingShingle	Darveen Vijayan misc TK7885-7895 misc QA75.5-76.95 misc moving points misc HDBSCAN misc crowd clustering misc unsupervised learning misc Computer engineering. Computer hardware misc Electronic computers. Computer science Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
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topic_title	TK7885-7895 QA75.5-76.95 Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications moving points HDBSCAN crowd clustering unsupervised learning
topic	misc TK7885-7895 misc QA75.5-76.95 misc moving points misc HDBSCAN misc crowd clustering misc unsupervised learning misc Computer engineering. Computer hardware misc Electronic computers. Computer science
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title	Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
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title_full	Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
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title_sort	adaptive hierarchical density-based spatial clustering algorithm for streaming applications
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title_auth	Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
abstract	Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including the algorithm’s high computation cost. In other words, the algorithm may be impractical for real-time or near-real-time implementation. Furthermore, clustering algorithms necessitate the tuning of hyperparameters in order to fit the dataset. In this paper, we approach clustering moving points using our proposed Adaptive Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, which is an implementation of an adaptive approach to building the minimum spanning tree. We switch between the Boruvka and the Prim algorithms as a means to build the minimum spanning tree, which is one of the most expensive components of the HDBSCAN. The Adaptive HDBSCAN yields an improvement in execution time by 5.31% without depreciating the accuracy of the algorithm. The motivation for this research stems from the desire to cluster moving points on video. Cameras are used to monitor crowds and improve public safety. We can identify potential risks due to overcrowding and movements of groups of people by understanding the movements and flow of crowds. Surveillance equipment combined with deep learning algorithms can assist in addressing this issue by detecting people or objects, and the Adaptive HDBSCAN is used to cluster these items in real time to generate information about the clusters.
abstractGer	Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including the algorithm’s high computation cost. In other words, the algorithm may be impractical for real-time or near-real-time implementation. Furthermore, clustering algorithms necessitate the tuning of hyperparameters in order to fit the dataset. In this paper, we approach clustering moving points using our proposed Adaptive Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, which is an implementation of an adaptive approach to building the minimum spanning tree. We switch between the Boruvka and the Prim algorithms as a means to build the minimum spanning tree, which is one of the most expensive components of the HDBSCAN. The Adaptive HDBSCAN yields an improvement in execution time by 5.31% without depreciating the accuracy of the algorithm. The motivation for this research stems from the desire to cluster moving points on video. Cameras are used to monitor crowds and improve public safety. We can identify potential risks due to overcrowding and movements of groups of people by understanding the movements and flow of crowds. Surveillance equipment combined with deep learning algorithms can assist in addressing this issue by detecting people or objects, and the Adaptive HDBSCAN is used to cluster these items in real time to generate information about the clusters.
abstract_unstemmed	Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including the algorithm’s high computation cost. In other words, the algorithm may be impractical for real-time or near-real-time implementation. Furthermore, clustering algorithms necessitate the tuning of hyperparameters in order to fit the dataset. In this paper, we approach clustering moving points using our proposed Adaptive Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, which is an implementation of an adaptive approach to building the minimum spanning tree. We switch between the Boruvka and the Prim algorithms as a means to build the minimum spanning tree, which is one of the most expensive components of the HDBSCAN. The Adaptive HDBSCAN yields an improvement in execution time by 5.31% without depreciating the accuracy of the algorithm. The motivation for this research stems from the desire to cluster moving points on video. Cameras are used to monitor crowds and improve public safety. We can identify potential risks due to overcrowding and movements of groups of people by understanding the movements and flow of crowds. Surveillance equipment combined with deep learning algorithms can assist in addressing this issue by detecting people or objects, and the Adaptive HDBSCAN is used to cluster these items in real time to generate information about the clusters.
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title_short	Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
url	https://doi.org/10.3390/telecom4010001 https://doaj.org/article/583329ee1d504d4c84ceaf92315916ea https://www.mdpi.com/2673-4001/4/1/1 https://doaj.org/toc/2673-4001
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doi_str	10.3390/telecom4010001
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