Develop a dynamic DBSCAN algorithm for solving initial parameter selection problem of the DBSCAN algorithm

Hossain, Md. Zakir; Islam, Md. Jakirul; Miah, Md. Waliur Rahman; Rony, Jahid Hasan; Begum, Momotaz

doi:10.11591/ijeecs.v23.i3.pp1602-1610

Cited by 6 publications

(4 citation statements)

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“…The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is a widely used clustering method known for its ability to handle noisy data, arbitrary shapes, and outlier detection (Hossain et al, 2021). DBSCAN operates based on the concept of density clustering in unsupervised learning, forming clusters by grouping closely packed data points separated by areas of low density (Lai et al, 2019).…”

Section: Results Of Machine Learning Clusteringmentioning

confidence: 99%

Manual Label and Machine Learning in Clustering and Predicting Student Performance

Yin,

Cao,

et al. 2024

International Journal of Web-Based Learning and Teaching Technologies

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This study presents the Academic Investment Model (AIM) as a novel approach to predicting student academic performance by incorporating learning styles as a predictive feature. Utilizing data from 138 Marketing students across China, the research employs a combination of machine learning clustering methods and manual feature engineering through a four-quadrant clustering technique. The AIM model delineates student investment into four quadrants based on their time and energy commitment to academic pursuits, distinguishing between result-oriented and process-oriented investments. The findings reveal that the four-quadrant method surpasses machine learning clustering in predictive accuracy, highlighting the robustness of manual feature engineering. The study's significance lies in its potential to guide educators in designing targeted interventions and personalized learning strategies, emphasizing the importance of process-oriented assessment in education. Future research is recommended to expand the sample size and explore the integration of deep learning models for validation.

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Section: Results Of Machine Learning Clusteringmentioning

confidence: 99%

Manual Label and Machine Learning in Clustering and Predicting Student Performance

Yin,

Cao,

et al. 2024

International Journal of Web-Based Learning and Teaching Technologies

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“…Points that are within a chain of distance ε are considered density-reachable and density-connected; notably, point Z can be density-reachable from point X. Thus, this method creates clusters by connecting core points and their neighbors in dense regions within a distance of ε [ 66 ].…”

Section: Methodsmentioning

confidence: 99%

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

Suleymanoglu,

Soycan,

Toth

2024

Sensors

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The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels.

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“…The fitness function is applied to determine the best values of both parameters. Another interesting work in determining the two DBSCAN parameters is done by Hossain et al [15] in which the mean value of the distance for a data point to all other points is calculated and this is repeated for all data points. The data point with the smallest minimum value is considered as having the highest density.…”

Section: Related Workmentioning

confidence: 99%

An Alternative Parameter Free Algorithm to DBSCAN Method by Using Data Point Positioning Analysis (DBSCAN-DPPA)

Mustapha

2022

Preprint

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Density-based spatial clustering of applications with noise (DBSCAN) is a powerful unsupervised clustering method for its ability to manage noises and arbitrary cluster shapes without the need to pre-determine the total clusters. However, the performance of DBSCAN is dependable to right choice of its two initial parameters – MinPts and Eps. Much research had been done to overcome the challenges by reducing the dependencies of these two parameters or automatically determine the values. This paper will review some of these various techniques related to reducing the dependencies of two DBSCAN initial parameters and subsequently proposes the new algorithm called DBSCAN-DPPA which do not require the initial setting of the two parameters by using Data Point Positioning Analysis. The algorithm is simple as it does not need any parameters to be initially assigned manually as it performs analysis on the positions of data points to determine the 1-NN and Max-NN. The algorithm is applied on 13 benchmark datasets that have been applied in many clustering algorithms. The performance of the algorithm is visually compared with the three-dimensional graph plotting at various angles to prove the number of clusters. The results show that the modified DBSCAN-DPPA algorithms are comparable to the performance of the traditional DBSCAN algorithm such that it manages to detect arbitrary cluster shapes, identify the number of clusters and manage the data sets with noises.

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Develop a dynamic DBSCAN algorithm for solving initial parameter selection problem of the DBSCAN algorithm

Cited by 6 publications

References 24 publications

Manual Label and Machine Learning in Clustering and Predicting Student Performance

Manual Label and Machine Learning in Clustering and Predicting Student Performance

3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds

An Alternative Parameter Free Algorithm to DBSCAN Method by Using Data Point Positioning Analysis (DBSCAN-DPPA)

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