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	A local-density based spatial clustering algorithm with noise
	Duan, Lian; Xu, Lida; Guo, Feng; Lee, Jun; Yan, Baopin
刊名	Information systems
	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
DOI	10.1016/j.is.2006.10.006
通讯作者	Duan, lian(duanlian@cstnet.cn)
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关键词	FEATURE SPACE THEORY
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000248769100004
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2374095
专题	计算机网络信息中心
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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