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- W2912456704 abstract "DBSCAN is one of clustering algorithms which can report arbitrarily-shaped clusters and noises without requiring the number of clusters as a parameter (unlike the other clustering algorithms, k-means, for example). Because the running time of DBSCAN has quadratic order of growth, i.e. O(n2), research studies on improving its performance have been received a considerable amount of attention for decades. Grid-based DBSCAN is a well-developed algorithm whose complexity is improved to O(nlog n) in 2D space, while requiring Ω(n4/3) to solve when dimension ≥ 3. However, we find that Grid-based DBSCAN suffers from two problems: neighbour explosion and redundancies in merging, which make the algorithms infeasible in high dimensional space. In this paper we first propose a novel algorithm called GDCF which utilizes bitmap indexing to support efficient neighbour grid queries. Second, based on the concept of union-find algorithm we devise a forest-like structure, called cluster forest, to alleviate the redundancies in the merging. Moreover, we find that running the cluster forest in different orders can lead to a different number of merging operations needed to perform in the merging step. We propose to perform the merging step in a uniform random order to optimize the number of merging operations. However, for high-density database, a bottleneck could be occurred, we further propose a low-density-first order to alleviate this bottleneck. The experiments resulted on both real-world and synthetic datasets demonstrate that the proposed algorithm outperforms the state-of-the-art exact/approximate DBSCAN and suggests a good scalability." @default.
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- W2912456704 date "2019-06-01" @default.
- W2912456704 modified "2023-10-16" @default.
- W2912456704 title "Grid-based DBSCAN: Indexing and inference" @default.
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- W2912456704 doi "https://doi.org/10.1016/j.patcog.2019.01.034" @default.
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