{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T04:08:06Z","timestamp":1747368486943,"version":"3.40.5"},"reference-count":0,"publisher":"University of Florida George A Smathers Libraries","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["FLAIRS"],"abstract":"<jats:p>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.\u00a0However, 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.\u00a0This 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.\u00a0Building 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.\u00a0The 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.\u00a0Clusters 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.\u00a0Empirical results demonstrate that the proposed approach outperforms traditional DBSCAN, as evidenced by lower Davies-Bouldin indices and higher silhouette scores.\u00a0This 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.<\/jats:p>","DOI":"10.32473\/flairs.38.1.138998","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T15:20:53Z","timestamp":1747322453000},"source":"Crossref","is-referenced-by-count":0,"title":["Kernel Density Based Spatial Clustering of Applications with Noise"],"prefix":"10.32473","volume":"38","author":[{"given":"Rohan","family":"Kalpavruksha","sequence":"first","affiliation":[]},{"given":"Roshan","family":"Kalpavruksha","sequence":"additional","affiliation":[]},{"given":"Teryn","family":"Cha","sequence":"additional","affiliation":[]},{"given":"Sung-Hyuk","family":"Cha","sequence":"additional","affiliation":[]}],"member":"17357","published-online":{"date-parts":[[2025,5,14]]},"container-title":["The International FLAIRS Conference Proceedings"],"original-title":[],"link":[{"URL":"https:\/\/journals.flvc.org\/FLAIRS\/article\/download\/138998\/144079","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.flvc.org\/FLAIRS\/article\/download\/138998\/144079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T15:20:54Z","timestamp":1747322454000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.flvc.org\/FLAIRS\/article\/view\/138998"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.32473\/flairs.38.1.138998","relation":{},"ISSN":["2334-0762","2334-0754"],"issn-type":[{"value":"2334-0762","type":"electronic"},{"value":"2334-0754","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5,14]]}}}