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DBSCAN algorithm is used widely because it can effectively handle noise points and deal with data of any type in clustering. However, it has two inherent limitations: high time complexity O(NlogN) and poor ability in dealing large-scale data. In this paper, a linear DBSCAN based on LSH is proposed. In our algorithm the process of Nearest Neighbor Search is optimized by hashing. Compared with the original...
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