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A local-density based spatial clustering algorithm with noise

文献类型：期刊论文

作者Duan, Lian; Xu, Lida; Guo, Feng; Lee, Jun; Yan, Baopin
刊名INFORMATION SYSTEMS
出版日期2007-11-01
卷号32期号:7页码:978-986
关键词data mining local outlier factor local reachability density local-density-based clustering
ISSN号0306-4379
DOI10.1016/j.is.2006.10.006
英文摘要Density-based clustering algorithms are attractive for the task of class identification in spatial database. However, in many cases, very different local-density clusters exist in different regions of data space, therefore, DBSCAN method [M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: E. Simoudis, J. Han, U.M. Fayyad (Eds.), Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI, Menlo Park, CA, 1996, pp. 226-231] using a global density parameter is not suitable. Although OPTICS [M. Ankerst, M.M. Breunig, H.-P. Kriegel, J. Sander, OPTICS: ordering points to identify the clustering structure, in: A. Delis, C. Faloutsos, S. Ghandeharizadeh (Eds.), Proceedings of ACM SIGMOD International Conference on Management of Data Philadelphia, PA, ACM, New York, 1999, pp. 49-60] provides an augmented ordering of the database to represent its density-based clustering structure, it only generates the clusters with local-density exceeds certain thresholds but not the cluster of similar local-density; in addition, it does not produce clusters of a data set explicitly. Furthermore, the parameters required by almost all the major clustering algorithms are hard to determine although they significantly impact on the clustering result. In this paper, a new clustering algorithm LDBSCAN relying on a local-density-based notion of clusters is proposed. In this technique, the selection of appropriate parameters is not difficult, it also takes the advantage of the LOF [M.M. Breunig, H.-P. Kriegel, R.T. Ng, J. Sander, LOF: identifying density-based local outliers, in: W. Chen, J.F. Naughton, P.A. Bernstein (Eds.), Proceedings of ACM SIGMOD International Conference on Management of Data, Dalles, TX, ACM, New York, 2000, pp. 93-104] to detect the noises comparing with other density-based clustering algorithms. The proposed algorithm has potential applications in business intelligence. (c) 2006 Elsevier B.V. All rights reserved.
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000248769100004
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/10924]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Duan, Lian
作者单位1.Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Zhejiang Univ, Hangzhou 310027, Peoples R China
4.Old Dominion Univ, Norfolk, VA 23529 USA
推荐引用方式
GB/T 7714		 Duan, Lian,Xu, Lida,Guo, Feng,et al. A local-density based spatial clustering algorithm with noise[J]. INFORMATION SYSTEMS,2007,32(7):978-986.
APA Duan, Lian,Xu, Lida,Guo, Feng,Lee, Jun,&Yan, Baopin.(2007).A local-density based spatial clustering algorithm with noise.INFORMATION SYSTEMS,32(7),978-986.
MLA Duan, Lian,et al."A local-density based spatial clustering algorithm with noise".INFORMATION SYSTEMS 32.7(2007):978-986.

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来源：计算技术研究所

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