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IEEE/ICACT20260308 Slide.10        [Big Slide]	Oral Presentation
	Before explaining our previous work, I will briefly introduce the original DBSCAN algorithm. DBSCAN is a density-based clustering algorithm that groups data points which are located close to each other. It defines clusters using two parameters: epsilon, which represents the neighborhood radius, and MinPts, which is the minimum number of points required within that radius. Points that satisfy this density condition form a cluster, while points that do not belong to any cluster are classified as noise.

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