DBSCAN is arguably the most popular density-based clustering algorithm, and it capable of recovering non-spherical clusters. One its main weaknesses that treats all features equally. In this paper, we propose a algorithm calculating feature weights representing degree relevance each feature, which takes density structure data into account. First, improve introduce new called DBSCANR. DBSCANR re...

DBSCAN is a fundamental spatial clustering algorithm with numerous practical applications. However, bottleneck of its O(n2) worst-case time complexity. To address this limitation, we propose new grid-based for exact in Euclidean space called GriT-DBSCAN, which based on the following two techniques. First, introduce grid tree to organize non-empty grids purpose efficient neighboring queries. Sec...