Consistency and Rates for Clustering with DBSCAN
نویسندگان
چکیده
We propose a simple and efficient modification of the popular DBSCAN clustering algorithm. This modification is able to detect the most interesting vertical threshold level in an automated, data-driven way. We establish both consistency and optimal learning rates for this modification.
منابع مشابه
بررسی مشکلات الگوریتم خوشه بندی DBSCAN و مروری بر بهبودهای ارائهشده برای آن
Clustering is an important knowledge discovery technique in the database. Density-based clustering algorithms are one of the main methods for clustering in data mining. These algorithms have some special features including being independent from the shape of the clusters, highly understandable and ease of use. DBSCAN is a base algorithm for density-based clustering algorithms. DBSCAN is able to...
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