信息可视化技术与数据采掘集成研究综述
On Integrating Information Visualization Techniques into Data Mining: A Review
摘要：
信息时代数字数据的爆炸式增长及其不可估量的潜在价值要求不同类型的数据驱动技术来挖掘其进一步的应用价值。信息可视化和数据采掘就是以此为目标的两个研究领域。虽然这两个社区倡导不同的解决问题的方法，结合数据采掘的复杂算法技术以及信息可视化的直观性和交互性的愿景是诱人的。在本文中，我们试图调查最近的研究和现实世界的系统集成的智慧，在两个领域，以更有效和更高效的数据分析。更具体地说，我们研究的交集从数据采掘的角度来看，探索如何信息可视化可以用来补充和改善数据采掘的不同阶段，通过建立优化的可视化表示的理论，以及实用的工具集，快速开发。我们组织的调查，确定了三个主要阶段的典型过程的数据采掘，初步分析的数据，模型的建设，以及模型的评估，并研究如何每个阶段可以受益于信息可视化。
英文摘要：
The exploding growth of digital data in the information era and its immeasurable potential value has called for different types of data-driven techniques to exploit its value for further applications. Information visualization and data mining are two research field with such goal. While the two communities advocates different approaches of problem solving, the vision of combining the sophisticated algorithmic techniques from data mining as well as the intuitivity and interactivity of information visualization is tempting. In this paper, we attempt to survey recent researches and real world systems integrating the wisdom in two fields towards more effective and efficient data analytics. More specifically, we study the intersection from a data mining point of view, explore how information visualization can be used to complement and improve different stages of data mining through established theories for optimized visual presentation as well as practical toolsets for rapid development. We organize the survey by identifying three main stages of typical process of data mining, the preliminary analysis of data, the model construction, as well as the model evaluation, and study how each stage can benefit from information visualization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
