机构

• 地理科学与资源研究所 [2]
• 计算技术研究所 [2]
• 国家空间科学中心 [1]
• 遥感与数字地球研究所 [1]
• 自动化研究所 [1]
• 武汉岩土力学研究所 [1]
• 更多

采集方式

• OAI收割 [9]

内容类型

• 期刊论文 [8]
• 会议论文 [1]

发表日期

• 2024 [1]
• 2021 [4]
• 2019 [1]
• 2018 [1]
• 2017 [1]
• 2008 [1]
• 更多

学科主题

筛选

浏览/检索结果: 共9条，第1-9条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序作者升序作者降序 Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks 期刊论文  OAI收割 GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES, 2024, 卷号: 10, 期号: 1, 页码: 22 作者:  Ashraf, Umar;  Anees, Aqsa;  Zhang, Hucai;  Ali, Muhammad;  Thanh, Hung Vo   |  收藏  |  浏览/下载：1/0  |  提交时间：2025/06/27 Unsupervised ML algorithms  SOM  K-means clustering  Payable clusters distribution  DBSCAN  SHAP   Gas Station Recognition Method Based on Monitoring Data of Heavy-Duty Vehicles 期刊论文  OAI收割 ENERGIES, 2021, 卷号: 14 作者:  Ding, Yan;  Ji, Zhe;  Liu, Peng;  Wu, Zhiqiang;  Li, Gang   |  收藏  |  浏览/下载：65/0  |  提交时间：2022/02/14 gas stations recognition  oil quality evaluation  heavy-duty vehicles  DBSCAN clustering  CART algorithm  real-world data   Clustering Indoor Positioning Data Using E-DBSCAN 期刊论文  OAI收割 ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 卷号: 10, 期号: 10, 页码: 25 作者:  Cheng, Dayu;  Yue, Guo;  Pei, Tao;  Wu, Mingbo   |  收藏  |  浏览/下载：34/0  |  提交时间：2022/09/21 indoor positioning data  spatial-temporal mobility  weighted edit distance  E-DBSCAN  trajectory clustering   Clustering Indoor Positioning Data Using E-DBSCAN 期刊论文  OAI收割 ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 卷号: 10, 期号: 10, 页码: 25 作者:  Cheng, Dayu;  Yue, Guo;  Pei, Tao;  Wu, Mingbo   |  收藏  |  浏览/下载：39/0  |  提交时间：2022/09/21 indoor positioning data  spatial-temporal mobility  weighted edit distance  E-DBSCAN  trajectory clustering   HTDet: A Clustering Method Using Information Entropy for Hardware Trojan Detection 期刊论文  OAI收割 TSINGHUA SCIENCE AND TECHNOLOGY, 2021, 卷号: 26, 期号: 1, 页码: 48-61 作者:  Lu, Renjie;  Shen, Haihua;  Feng, Zhihua;  Li, Huawei;  Zhao, Wei   |  收藏  |  浏览/下载：59/0  |  提交时间：2021/12/01 Hardware Trojan (HT) detection  information entropy  Density-Based Spatial Clustering of Applications with Noise (DBSCAN)  unsupervised learning  clustering  mutual information  test patterns generation   Grid-based DBSCAN: Indexing and inference 期刊论文  OAI收割 PATTERN RECOGNITION, 2019, 卷号: 90, 页码: 271-284 作者:  Zhuang, Fuzhen;  He, Qing;  Boonchoo, Thapana;  Ao, Xiang;  Liu, Yang   |  收藏  |  浏览/下载：83/0  |  提交时间：2019/08/16 Density-based clustering  Grid-based DBSCAN  Union-find algorithm   GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing 期刊论文  OAI收割 Pattern Recognition Letters, 2018, 卷号: 109, 页码: 81-88 作者:  Deng, Chao;  Song, Jinwei;  Sun, Ruizhi;  Cai, Saihua;  Shi, Yinxue   |  收藏  |  浏览/下载：166/0  |  提交时间：2018/02/28 Grid Based Clustering  Density Based Clustering  Dbscan  Griden  Data Mining  Massive Spatial Data  Parallel Computing   An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference 期刊论文  OAI收割 International Journal of Automation and Computing, 2017, 卷号: 14, 期号: 1, 页码: 57-67 作者:  S. Nagaraju;  Manish Kashyap;  Mahua Bhattachraya   |  收藏  |  浏览/下载：16/0  |  提交时间：2021/02/23 Density based clustering  neighborhood difference  density-based spatial clustering of applications with noise (DBSCAN)  space density indexing (SDI)  core object.   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：27/0  |  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.