Self-extraction of rockburst knowledge of mining at great depth using data mining

被引：0
|
作者：
Ma, Ping-bo [1 ]
Feng, Xia-ting [1 ]
Zhang, Zhi-qiang [1 ]
Liu, Yan-chao [1 ]
Chen, Ting-wei [1 ]
机构：
[1] Northeastern Univ, Shenyang, China
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2000年 / 21卷 / 06期
关键词：
Artificial intelligence - Expert systems - Feature extraction - Risk assessment;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Rockburst induced by mining at great depth is very complicated and affected by many factors. A new method for self-extraction of knowledge for assessing rockburst risks induced by mining was proposed with thoughts of rock mechanics and artificial intelligence. Knowledge is extracted from data cases and combined to the integrated intelligent system on rockburst risk assessment developed before. Therefore, this system can be used to assess reasonably risks of rockburst induced by mining using monitored information in site.
引用
收藏
页码：630 / 633
相关论文
共 50 条
  • [1] A review on Knowledge extraction for Business operations using Data Mining
    Bharara, Sanyam
    Sabitha, A. Sai
    Bansal, Abhay
    [J]. PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE AND ENGINEERING (CONFLUENCE 2017), 2017, : 512 - 518
  • [2] A distributed knowledge extraction data mining algorithm
    Liu, JB
    Thanneru, U
    Cheng, DZ
    [J]. COMPUTATIONAL AND INFORMATION SCIENCE, PROCEEDINGS, 2004, 3314 : 768 - 774
  • [3] Challenges of mining at great depth
    Rymon-Lipinski, WK
    [J]. MINING IN THE NEW MILLENNIUM CHALLENGES AND OPPORTUNITIES, 2000, : 11 - 22
  • [4] Knowledge Extraction Using Web Usage Mining
    Waqas, Muhammad
    Iram, Maria
    Shahzad, Sara
    Arshad, Sidra
    Nawaz, Tahir
    [J]. EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS, 2018, 4 (16) : 1 - 5
  • [5] Data mining for knowledge discovery in mining
    Golosinski, TS
    Hu, H
    [J]. MINE PLANNING AND EQUIPMENT SELECTION 2001, 2001, : 1011 - 1018
  • [6] Extraction of information and knowledge production through Data Mining
    Presser, Nadi Helena
    da Silva, Eli Lopes
    [J]. NAVUS-REVISTA DE GESTAO E TECNOLOGIA, 2018, 8 (01): : 5 - 6
  • [7] Data mining toolkit for extraction of knowledge from LMS
    Villegas-Ch, W.
    Lujan-Mora, S.
    Buenano-Fernandez, Diego
    [J]. PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON EDUCATION TECHNOLOGY AND COMPUTERS (ICETC 2017), 2017, : 31 - 35
  • [8] Combining Web Data Extraction and Data Mining Techniques to Discover Knowledge
    Bouldoukian, Nathalie A.
    [J]. 2018 IEEE MIDDLE EAST AND NORTH AFRICA COMMUNICATIONS CONFERENCE (MENACOMM), 2018, : 170 - 175
  • [9] Knowledge Extraction and Data Visualization: A Proposed Framework for Secure Decision Making using Data Mining
    Alshareef, Hazzaa N.
    Majrashi, Ahmed
    Helal, Maha
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (08) : 483 - 489
  • [10] Discovering case knowledge using Data Mining
    Anand, SS
    Patterson, D
    Hughes, JG
    Bell, DA
    [J]. RESEARCH AND DEVELOPMENT IN KNOWLEDGE DISCOVERY AND DATA MINING, 1998, 1394 : 25 - 35
12345