首页> 外文会议>IFIP TC 13 international conference on human-computer interaction;INTERACT 2011 >Exploring New Ways of Utilizing Automated Clustering and Machine Learning Techniques in Information Visualization
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Exploring New Ways of Utilizing Automated Clustering and Machine Learning Techniques in Information Visualization

机译:在信息可视化中探索利用自动聚类和机器学习技术的新方法

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Research Topic. The main research topic of the thesis is to explore the possibilities of automated clustering and machine learning techniques for developing new approaches in information visualization. Research Problem. The main goal of information visualization is to present data to the users in a way that optimizes intelligibility of the data and support the detection of relevant patterns in the data, where the application context defines what qualifies as 'relevant'. Many different approaches typically tailored to a specific problem have been developed within the past years. At the same time the application of mathematical methods for data analysis and identification of patterns has substantially increased, and is typically referred to as data mining. Different visualization techniques are used in data mining, however the systematic and dynamic integration of data mining techniques with visualization approaches is only in its beginning.
机译:研究课题。本论文的主要研究主题是探索自动聚类和机器学习技术为开发信息可视化新方法的可能性。研究问题。信息可视化的主要目标是以优化数据的清晰度并支持检测数据中相关模式的方式向用户显示数据,其中应用程序上下文定义了什么才是“相关”。在过去的几年中,已经开发出许多通常针对特定问题量身定制的不同方法。同时,用于数据分析和模式识别的数学方法的应用已大大增加,通常称为数据挖掘。数据挖掘中使用了不同的可视化技术,但是将数据挖掘技术与可视化方法进行系统和动态的集成才刚刚开始。

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