Kernel Density Based Spatial Clustering of Applications with Noise
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used clustering algorithm renowned for its ability to identify clusters of arbitrary shapes and detect noise. However, its reliance on fixed parameters, such as the minimum number of points (MinPts) and the epsilon rad...
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LibraryPress@UF
2025-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/138998 |
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| author | Rohan Kalpavruksha Roshan Kalpavruksha Teryn Cha Sung-Hyuk Cha |
| author_facet | Rohan Kalpavruksha Roshan Kalpavruksha Teryn Cha Sung-Hyuk Cha |
| author_sort | Rohan Kalpavruksha |
| collection | DOAJ |
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Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used clustering algorithm renowned for its ability to identify clusters of arbitrary shapes and detect noise. However, its reliance on fixed parameters, such as the minimum number of points (MinPts) and the epsilon radius (epsilon), makes it sensitive to variations in sample density. This paper reinterprets DBSCAN as a specific case of kernel density estimation (KDE)-based clustering, where the kernel shape corresponds to a hyper-rectangular pillar or cylindrical kernel, depending on the distance metric. Building on this foundation, we introduce a flexible framework incorporating various kernel functions, including uniform, conical, Epanechnikov, cosine, exponential, and Gaussian kernels, to estimate the density distribution of data points. The threshold values are selected to identify high-density regions by retaining the top 90% of points, while excluding low-density points as noise, thereby enhancing clustering precision. Clusters are adaptively formed by leveraging points within the kernel range, thereby increasing the algorithm's robustness to noise and its adaptability to irregular density patterns. Empirical results demonstrate that the proposed approach outperforms traditional DBSCAN, as evidenced by lower Davies-Bouldin indices and higher silhouette scores. This study highlights the potential of density-driven clustering for practical applications, including social media sentiment analysis, customer segmentation in e-commerce, and medical data analysis, particularly in scenarios involving noise-prone or unevenly distributed datasets.
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| format | Article |
| id | doaj-art-4b00fb0cb16e47aeadbb3916ae8e5585 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-4b00fb0cb16e47aeadbb3916ae8e55852025-08-20T03:09:57ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622025-05-0138110.32473/flairs.38.1.138998Kernel Density Based Spatial Clustering of Applications with NoiseRohan KalpavrukshaRoshan KalpavrukshaTeryn ChaSung-Hyuk Cha0Pace University Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used clustering algorithm renowned for its ability to identify clusters of arbitrary shapes and detect noise. However, its reliance on fixed parameters, such as the minimum number of points (MinPts) and the epsilon radius (epsilon), makes it sensitive to variations in sample density. This paper reinterprets DBSCAN as a specific case of kernel density estimation (KDE)-based clustering, where the kernel shape corresponds to a hyper-rectangular pillar or cylindrical kernel, depending on the distance metric. Building on this foundation, we introduce a flexible framework incorporating various kernel functions, including uniform, conical, Epanechnikov, cosine, exponential, and Gaussian kernels, to estimate the density distribution of data points. The threshold values are selected to identify high-density regions by retaining the top 90% of points, while excluding low-density points as noise, thereby enhancing clustering precision. Clusters are adaptively formed by leveraging points within the kernel range, thereby increasing the algorithm's robustness to noise and its adaptability to irregular density patterns. Empirical results demonstrate that the proposed approach outperforms traditional DBSCAN, as evidenced by lower Davies-Bouldin indices and higher silhouette scores. This study highlights the potential of density-driven clustering for practical applications, including social media sentiment analysis, customer segmentation in e-commerce, and medical data analysis, particularly in scenarios involving noise-prone or unevenly distributed datasets. https://journals.flvc.org/FLAIRS/article/view/138998ClusteringDBSCANKernel |
| spellingShingle | Rohan Kalpavruksha Roshan Kalpavruksha Teryn Cha Sung-Hyuk Cha Kernel Density Based Spatial Clustering of Applications with Noise Proceedings of the International Florida Artificial Intelligence Research Society Conference Clustering DBSCAN Kernel |
| title | Kernel Density Based Spatial Clustering of Applications with Noise |
| title_full | Kernel Density Based Spatial Clustering of Applications with Noise |
| title_fullStr | Kernel Density Based Spatial Clustering of Applications with Noise |
| title_full_unstemmed | Kernel Density Based Spatial Clustering of Applications with Noise |
| title_short | Kernel Density Based Spatial Clustering of Applications with Noise |
| title_sort | kernel density based spatial clustering of applications with noise |
| topic | Clustering DBSCAN Kernel |
| url | https://journals.flvc.org/FLAIRS/article/view/138998 |
| work_keys_str_mv | AT rohankalpavruksha kerneldensitybasedspatialclusteringofapplicationswithnoise AT roshankalpavruksha kerneldensitybasedspatialclusteringofapplicationswithnoise AT teryncha kerneldensitybasedspatialclusteringofapplicationswithnoise AT sunghyukcha kerneldensitybasedspatialclusteringofapplicationswithnoise |