机构

• 计算技术研究所 [1]
• 理论物理研究所 [1]
• 数学与系统科学研究院 [1]
• 遥感与数字地球研究所 [1]
• 中国科学院大学 [1]

采集方式

• OAI收割 [4]
• iSwitch采集 [1]

内容类型

• 期刊论文 [4]
• 会议论文 [1]

发表日期

• 2016 [1]
• 2015 [1]
• 2010 [2]
• 2008 [1]

学科主题

• Mechanics [1]
• Physics [1]

筛选

浏览/检索结果: 共5条，第1-5条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 A corrected normalized mutual information for performance evaluation of community detection 期刊论文  iSwitch采集 Journal of statistical mechanics-theory and experiment, 2016, 页码: 20 作者:  Lai, Darong;  Nardini, Christine 收藏  |  浏览/下载：25/0  |  提交时间：2019/05/09 Clustering techniques  Random graphs  Networks   Evaluating accuracy of community detection using the relative normalized mutual information 期刊论文  OAI收割 JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2015, 期号: 0, 页码: P11006 作者:  Zhang, P 收藏  |  浏览/下载：33/0  |  提交时间：2016/11/21 random graphs  networks  clustering techniques   Weighted tunable clustering in local-world networks with increment behavior 期刊论文  OAI收割 JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2010, 页码: 20 作者:  Ma, Ying-Hong;  Li, Huijia;  Zhang, Xiao-Dong   |  收藏  |  浏览/下载：12/0  |  提交时间：2018/07/30 growth processes  clustering techniques   Spectral methods for the detection of network community structure: a comparative analysis 期刊论文  OAI收割 JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2010, 页码: 13 作者:  Shen, Hua-Wei;  Cheng, Xue-Qi   |  收藏  |  浏览/下载：15/0  |  提交时间：2019/12/16 analysis of algorithms  random graphs  networks  clustering techniques   A novel spatial clustering algorithm based on Delaunay triangulation 会议论文  OAI收割 International Conference on Earth Observation Data Processing and Analysis, ICEODPA,, Wuhan, China, December 28, 2008 - December 30,2008 Yang, Xiankun; Cui, Weihong 收藏  |  浏览/下载：24/0  |  提交时间：2014/12/07 Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques. So far  a lot of spatial clustering algorithms have been proposed. In this paper we propose a robust spatial clustering algorithm named SCABDT (Spatial Clustering Algorithm Based on Delaunay Triangulation). SCABDT demonstrates important advantages over the previous works. First  it discovers even arbitrary shape of cluster distribution. Second  in order to execute SCABDT  we do not need to know any priori nature of distribution. Third  like DBSCAN  Experiments show that SCABDT does not require so much CPU processing time. Finally it handles efficiently outliers. 2008 SPIE.