数据挖掘概念与技术 第二版 韩家炜 第十一章aVisualMine.pptVIP

Data Mining: Concepts and Techniques Data Mining:  Concepts and Techniques — Chapter 11 — — Applications and Trends in Data Mining— Additional Theme: Visual Data Mining Jiawei Han and Micheline Kamber Department of Computer Science University of Illinois at Urbana-Champaign /~hanj ?2006 Jiawei Han and Micheline Kamber. All rights reserved. Visual Data Mining: An Overview What is Visual Data Mining? Survey of techniques Data Visualization Visualizing Data Mining Results Visual Data Mining What Is Visual Data Mining? Visual data mining “discovers implicit and useful knowledge from large data sets using data and/or knowledge visualization techniques” Data visualization + Data mining techniques Why Visual Data Mining? Advantages of human visual system Highly parallel processor Sophisticated reasoning engine Large knowledge base Can be used to comprehend data distributions, patterns, clusters, and outliers Why Not Only Visual Data Mining? Disadvantages of human visual system Needs training Not automated Intrinsic bias Limit of about 106 or 107 observations (Wegman 1995) Power of integration with analytical methods Scope of Visual Data Mining Visualization: Use of computer graphics to create visual images which aid in the understanding of complex, often massive representations of data Visual Data Mining: The process of discovering implicit but useful knowledge from large data sets using visualization techniques Purpose of Visualization Gain insight into an information space by mapping data onto graphical primitives Provide qualitative overview of large data sets Search for patterns, trends, structure, irregularities, relationships among data Help find interesting regions and suitable parameters for further quantitative analysis Provide a visual proof of computer representations derived Visual Data Mining Data Visualization Integration of visualization and data mining data visualization data mining result visualization data mining process visualization interactive