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巨量挖掘结果的Focus+Context展现 被引量:1

Mining Result Presentation in Focus+Context Style
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摘要 针对数据挖掘结果集数量巨大的特点,基于Focus+Context人机交互可视化技术实现结果集的B roadV iew and D etail on D em and导航可以有效帮助对挖掘结果的聚焦与探询,进而帮助用户快速找到所需要的信息。给出了基于规则层次凝聚类与Focus+Context技术的可视化系统方法与原形系统,有效地解决了巨量规则的有效浏览与可视化问题,避免了直接对规则元素图形显示效果不佳的问题,从而有利于挖掘的应用。 Aiming to large quantities of data mining result collection, Broad View and Detail on Demand navigation based on Focus + Context man-machine interaction visual technology implementation result collection can effectively help focusing and seeking to mining result ,furthermore help user rapidly finding the required message .give visible system method and the original system based on rule hierarchy agglomeration type and Focus+Context technology ,effectively resolve effective browsing and visible problem of large quantities rule ,prevent poor graphic display effect directly to rule element ,thus bing favor of mining application.
作者 刘发升 杨惠
机构地区 江西理工大学
出处 《电脑开发与应用》 2007年第1期13-15,共3页 Computer Development & Applications
基金 江西省自然科学基金资助项目(项目号:0411046) 江西省科技厅工业攻关项目(项目号:赣财教[2005]132号)
关键词 数据挖掘 巨量挖掘 Focus+Context data mining ,enormous amount mining ,Focus+Context
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参考文献5

  • 1Xifeng Yan, Hong Cheng, Jiawei Han et al.Summarizing itemset patterns : a profile-based approach. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Chicago, Illinois, USA:ACM Press, 2005. 314-323.
  • 2Ken Mcgarry. A survey of interestingness measurement for knowledge discovery. Knowledge Engineering Review. 2005,20(1) :39-61.
  • 3David Hand, Heikki Mannila, Padhraic Smyth.Principles of data mining. Beijing: China Machine Press, 2003.
  • 4Daniel A Keim. Information Visualization and Visual Data Mining. IEEE Transactions on Visualization and Computer Graphics, 2002,7(1) :100-107.
  • 5Staffan Bjork, tars Erik Holmquist, Johan Redstrom. A framework for focus + context visualization. In: Proceedings of the 1999 IEEE Symposium on Information Visualization. IEEE Press, Washington, DC, USA, 1999: 53-57

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