Spatial clustering with Density-Ordered tree
Spatial clustering with Density-Ordered tree
复制标题
密度有序树的空间聚类
DOI：
10.1016/j.physa.2016.05.041
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发表时间：
2016-10
期刊：
影响因子：
--
通讯作者：
Liu Z
中科院分区：
文献类型：
--
作者：
Cheng Q;Lu X;Liu Z
Clustering has emerged as an active research direction for knowledge discovery in spatial databases. Most spatial clustering methods become ineffective when inappropriate parameters are given or when datasets of diverse shapes and densities are provided. To address this issue, we propose a novel clustering method, called SCDOT (Spatial Clustering with Density-Ordered Tree). By projecting a dataset to a Density-Ordered Tree, SCDOT partitions the data into several relatively small sub-clusters with a box-plot method. A heuristic method is proposed to find the genuine clusters by repeatedly merging sub-clusters and an iteration strategy is utilized to automatically determine input parameters. Moreover, we also provide an innovative way to identify cluster center and noise. Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of SCDOT over the baseline methods.
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DOI：
10.1109/icpr.2006.741
发表时间：
2006-08
期刊：
18th International Conference on Pattern Recognition (ICPR'06)
影响因子：
--
作者：
P. Viswanath;Rajwala Pinkesh
通讯作者：
P. Viswanath;Rajwala Pinkesh
DOI：
10.4324/9780203468029_chapter_8
发表时间：
2001
期刊：
--
影响因子：
--
作者：
Jiawei Han;M. Kamber;A. Tung
通讯作者：
Jiawei Han;M. Kamber;A. Tung
DOI：
10.1016/j.patrec.2009.08.008
发表时间：
2009-12
期刊：
Pattern Recognit. Lett.
影响因子：
--
作者：
P. Viswanath;V. S. Babu
通讯作者：
P. Viswanath;V. S. Babu
DOI：
10.1101/003889
发表时间：
2014-06
期刊：
Chinese Science Bulletin = Kexue Tongbao
影响因子：
--
作者：
Lin Wang;Xiang Li
通讯作者：
Lin Wang;Xiang Li
影响因子：
2.5
作者：
Birant, Derya;Kut, Alp
通讯作者：
Kut, Alp