期刊文献+

云计算环境下的数据挖掘应用 被引量:9

Application of data mining in cloud computing environment
下载PDF
导出
摘要 云计算是一个新的商业模型,它可以提供无限的廉价存储和计算能力。而数据挖掘中面临的主要问题是项目集合的空间需求问题,并且其操作非常巨大。将数据挖掘技术应用到云计算环境中,可以按需从云服务运营商那里获取项目集合所需空间,从而解决了数据挖掘需要巨大空间的问题。文章论述和分析了将数据挖掘应用到云计算环境的有效性。 Cloud computing is a new business model. It can provides unlimited cheap storage and computing power. The main issue with data mining techniques is that the space required for the item set and there operations are very huge. Combine data mining techniques with cloud computing environment, then we can rent the space from the cloud providers on demand. This solution can solve the problem of huge space. This paper discusses and analyzes the effectiveness of the application of data mining to the cloud computing environment.
作者 石杰
出处 《微型机与应用》 2015年第5期13-15,共3页 Microcomputer & Its Applications
基金 山东省自然科学基金资助项目(ZR2013FM010)
关键词 数据挖掘 云计算 频繁模式 云存储 data mining cloud computing frequent pattern cloud storage
  • 相关文献

参考文献8

  • 1WEISS A.Computing in clouds[J].ACM Networker,2007,11(4):18-25.
  • 2BUYYA R,VENUGOPAL S.Market-oriented cloud computing:vision,hype,and reality for delivering IT services as computing utilities[C].Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications,2008:5-13.
  • 3BOHM C,BERCHTOLD S,MICHEL U.Multidimensional index structures in relational databases[C].in 1stInternational Conference on Data Warehousing and Knowledge Discovery,1999:51-70.
  • 4DEAN J,GHEMAWAT S,USENIX.Map Reduce:simplified data processing on large clusters[C].6th Symposium on Operating Systems Design and Implementation,2004:137-149.
  • 5Han J,Pei J,Yin Y.Mining frequent patterns without candidate generation[C].Proc.of ACM Int.Conf.on Management of data(SIGMOD),2000:1-12.
  • 6KAWUU W LIN,LUO Y C.Efficient strategies for manytask frequent pattern mining in cloud computing environments[C].Systems Man and Cybernetics(SMC),IEEE International Conference,2010(10):620-623.
  • 7李玲娟,张敏.云计算环境下关联规则挖掘算法的研究[J].计算机技术与发展,2011,21(2):43-46. 被引量:48
  • 8NAIR T R G,MADHURI K L.Data mining using hierarchical virtual k-means approach integrating data fragments in cloud computing environment[C].Cloud Computing and Intelligence Systems(CCIS),IEEE International Conference,2011(1):230-234.

二级参考文献9

  • 1刘华元,袁琴琴,王保保.并行数据挖掘算法综述[J].电子科技,2006,19(1):65-68. 被引量:15
  • 2Weiss A. Computing in Clouds[ J]. ACM Networker,2007,11 (4) : 18-25.
  • 3Buyya R, Yeo C S, Venugopal S. Market-Oriented Cloud Computing : Vision, Hype, and Reality for Delivering IT Services as Computing Utilities[ C ]//Proceedings of the 2008 10^th IEEE International Conference on High Performance Computing and Communications. [ s. l. ] : [ s. n. ] ,2008 : 5-13.
  • 4Apache. Hadoop [ EB/OL]. 2006. http://lucene, apache. org/hadoop/.
  • 5Dean J, Ghemawat S. Mapreduce: Simplified data processing on large clusters [ C ]//Proceedings of the 6th Symposium on Operating System Design and Implementation. San Francisco, California, USA : USENIX Association, 2004 : 137-150.
  • 6Wu X, Kumar V, Ghosh R J, et al. Top 10 algorithms in data mining[J]. Knowledge and Information Systems,2008,14 (1) :1-37.
  • 7Agrawal R, Sharer J C. Parallel Mining of Association Rules [ J]. IEEE Transactions on Knowledge and Data Engineering, 1996,8 ( 6 ) : 962- 969.
  • 8Aflori C, Craus M. Grid implementation of the Aprioti algorithm[ J]. Engineering Software,2007, 38( 5): 295-300.
  • 9王鄂,李铭.云计算下的海量数据挖掘研究[J].现代计算机,2009,15(11):22-25. 被引量:26

共引文献47

同被引文献67

  • 1张玲.浅析WEB日志数据挖掘技术[J].今日科苑,2009(17). 被引量:1
  • 2汪闽,骆剑承,周成虎,明冬萍,陈秋晓,沈占峰.结合高斯马尔可夫随机场纹理模型与支撑向量机在高分辨率遥感图像上提取道路网[J].遥感学报,2005,9(3):271-276. 被引量:43
  • 3王家耀,张雪萍,周海燕.一个用于空间聚类分析的遗传K-均值算法[J].计算机工程,2006,32(3):188-190. 被引量:19
  • 4李万新.Web日志数据挖掘在服务器安全方面的应用[J].中山大学学报论丛,2007,27(5):116-118. 被引量:5
  • 5KOPERSKI K, HAN J W. Discovery of spatial association rules in geographic information databases [C]. Procedings of the 4th International Symposium on Advances in Spatial Databases, 1995: 47-66.
  • 6SHEKHAR S, HUANG Y. Discovering spatial co-location patterns: a summary of results [C]. Procedings of the 7th International Symposium on Advances in Spatial and Tem- poral Databases, 2001:236-256.
  • 7Zhang Xueping, Du Haohua, Yang Tengfei, et al. A novel spatial clustering with obstacles constraints based on PNPSO and K-medoids [C]. Advances in Swarm Intelligence, Lec- ture Notes in Computer Science (LNCS), 2010. 476-483.
  • 8SHEKHAR S, SCHRATER P R, VATSAVAI R R, et al. Spatial contextual classification and prediction models for mining geospatial data[J]. IEEE Transactions on Multimedia, 2002, 4(2) :174-187.
  • 9Wei Hao,YEN I L,THURAISINGHA M B.Dynamic service and data migration in the clouds[C].IEEE COMPSAC,2009:134-136.
  • 10杨保华,戴王剑,曹亚伦.Docker技术入门与实践[M].北京:机械工业出版社,2015.

引证文献9

二级引证文献18

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部