NDLI: A Linear DBSCAN Algorithm Based on LSH

Content Provider	IEEE Xplore Digital Library
Author	Yi-Pu Wu Jin-Jiang Guo Xue-Jie Zhang
Copyright Year	2007
Description	Author affiliation: Yunnan Univ., Kunming (Yi-Pu Wu; Jin-Jiang Guo; Xue-Jie Zhang)
Abstract	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 DBSCAN algorithm, the time complexity of this improved DBSCAN descends to O(N). Experimentally, this improved DBSCAN makes a significant decrease in the running time while maintaining the Cluster quality of the results. Moreover, the speedup (the running time of original DBSCAN algorithm divided by the running time of improved algorithm) increases with the size and dimension of dataset, and the parameter Eps of our algorithm does not have a strong influence on the clustering result. These improved properties enable DBSCAN to be used in a large scope.
Starting Page	2608
Ending Page	2614
File Size	855739
Page Count	7
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370588
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Nearest neighbor searches Partitioning algorithms Machine learning algorithms Information retrieval Image segmentation Machine learning Cybernetics Computer science Large-scale systems Large-scale data LSH DBSCAN Clustering Unsupervised learning
Content Type	Text
Resource Type	Article

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