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基于标签的数据挖掘技术的研究

Research on the Data Mining Technology Based on Tag
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摘要 近年来,数据呈指数级增长,人们对数据的利用能力越来越高,特别是近几年兴起的大数据、云计算、人工智能等新兴技术汇集了人类有史以来最多最全的数据,但是如何从数据中发现各种关系与规则,从海量数据中找到更有价值的数据,是人们急需解决的问题。数据挖掘技术是解决这一问题的根本方法,而基于标签的数据挖掘技术是完整刻画描述人物特征的基本方法。 In recent years,the numbers have grown exponentially.People's ability to use data is getting higher and higher,especially in recent years,emerging technologies such as big data,cloud computing and artificial intelligence have collected the largest and most complete data in human history.However,how to discover all kinds of relationships and rules from data and find more valuable data from mass data is an urgent problem that people need to solve.Data mining technology is the fundamental method to solve this problem,and the data mining technology based on tag is the basic method to describe the characters completely.
作者 肖建军 邱瑞 肖崇星 XIAO Jian-jun;QIU Rui;XIAO Chong-xing(First Research Institute of the Ministry of Public Security of PRC,Beijing 100048,China)
出处 《中小企业管理与科技》 2020年第4期156-157,共2页 Management & Technology of SME
关键词 数据挖掘 大数据 标签 data mining big data tag
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  • 1苏金树,张博锋,徐昕.基于机器学习的文本分类技术研究进展[J].软件学报,2006,17(9):1848-1859. 被引量:383
  • 2Matthew R B, Luo Jie-bo, Shen Xi-peng, et al. Learning multi-la- bel scene classification[J]. Pattern Recognition, 2004(37) : 1757- 1771.
  • 3Zhang Min-ling, Zhou Zhi-hua. MI-kNN: A lazy learning ap- proach to multi-label learning [J]. Pattern Recognition, 2007 (40) : 2038-2048.
  • 4Xu Xin-shun, Jiang Yuan, Peng Liang, et al. Ensemble approach based on conditional random field for multi-label image and video annotation[C]//Proceedings of the 19th ACM international conference on Multimedia. Scottsdale, Arizona, USA, 2011: 1377-1380.
  • 5Wang Jing-dong, Zhao Ying-hai, Wu Xiu-qing, et al. A transduc- tive multi-label learning approach for video concept detection [J]. Pattern Recognition, 2011(44) : 10-11.
  • 6Snoek C, Worring M, Gemert J V, et al. The challenge problem for automated detection of 101semantic concepts in multimedia [C]//Proceedings of the ACM Multimedia. Santa Barbara, CA, USA, 2006 : 421-430.
  • 7Clare A,King R. Knowledge discovery in multblabel phenotype data[C]//Proceedings of the 5th European Conference on Prin- ciples of Data Mining and Knowledge Discovery (PKDD). Freiburg, Germany, 2001 : 42-53.
  • 8Elisseeff A, Weston J. A kernel method for multi-labelled classifi cation[J]. Advances in Neural Information Processing Systems, 2001(14) : 681-687.
  • 9Blockeel H, Schietgat L, Struyf J, et al. Decision trees for hierar- chical multilabel classification: A case study in functional ge- nomics[C] // Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4213 LNAI. 2006:18-29.
  • 10Cesa-Bianchi N, Gentile C, Zaniboni L. Hierarchical classifica- tion:combining bayes with svra[C] // Proceedings of the 23rd International Conference on Machine learning. 2006:177-184.

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