Information Visualization in Data Mining and Knowledge Discovery / Edition 1
by Usama Fayyad, Georges G. Grinstein, Andreas Wierse
Mainstream data mining techniques significantly limit the role of human reasoning and insight. Likewise, in data visualization, the role of computational analysis is relatively small. The power demonstrated individually by these approaches to knowledge discovery suggests that somehow uniting the two could lead to increased efficiency and more valuable results.
Overview
Mainstream data mining techniques significantly limit the role of human reasoning and insight. Likewise, in data visualization, the role of computational analysis is relatively small. The power demonstrated individually by these approaches to knowledge discovery suggests that somehow uniting the two could lead to increased efficiency and more valuable results. But is this true? How might it be achieved? And what are the consequences for data-dependent enterprises?
Information Visualization in Data Mining and Knowledge Discovery is the first book to ask and answer these thought-provoking questions. It is also the first book to explore the fertile ground of uniting data mining and data visualization principles in a new set of knowledge discovery techniques. Leading researchers from the fields of data mining, data visualization, and statistics present findings organized around topics introduced in two recent international knowledge discovery and data mining workshops. Collected and edited by three of the area's most influential figures, these chapters introduce the concepts and components of visualization, detail current efforts to include visualization and user interaction in data mining, and explore the potential for further synthesis of data mining algorithms and data visualization techniques. This incisive, groundbreaking research is sure to wield a strong influence in subsequent efforts in both academic and corporate settings.
Features
- Details advances made by leading researchers from the fields of data mining, data visualization, and statistics.
- Provides a useful introduction to the science of visualization, sketches the current role for visualization in data mining, and then takes a long look into its mostly untapped potential.
- Presents the findings of recent international KDD workshops as formal chapters that together comprise a complete, cohesive body of research.
- Offerss compelling and practical information for professionals and researchers in database technology, data mining, knowledge discovery, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, information retrieval, high-performance computing, and data visualization.
Product Details
- ISBN-13:
- 9781558606890
- Publisher:
- Elsevier Science & Technology Books
- Publication date:
- 08/01/2001
- Series:
- The Morgan Kaufmann Series in Data Management Systems
- Pages:
- 300
- Product dimensions:
- 7.10(w) x 9.07(h) x 1.17(d)
Table of Contents
Foreword from the Series Editor | ||
Foreword | ||
Introduction | 1 | |
1 | Introduction to Data Visualization | 21 |
2 | A Survey of Visualizations for High-Dimensional Data Mining | 47 |
3 | Evaluation of Visualization Systems | 83 |
4 | The Data Visualization Environment | 87 |
5 | Visualizing Massive Multivariate Time-Series Data | 95 |
6 | Portable Document Indexes | 99 |
7 | Character-Based Data Visualization for Data Mining | 103 |
8 | Visualization in the Knowledge Discovery Process | 121 |
9 | What Can Visualization Do for Data Mining? | 123 |
10 | Multidimensional Information Visualizations for Data Mining | 125 |
11 | Benchmark Development for the Evaluation of Visualization for Data Mining | 129 |
12 | Data Visualization for Decision Support Activities | 177 |
13 | A Visualization-Driven Approach for Strategic Knowledge Discovery | 181 |
14 | A Visual Metaphor for Knowledge Discovery: An Integrated Approach to Visualizing the Task, Data, and Results | 191 |
15 | Visualizing Data Mining Models | 205 |
16 | Model Visualization | 223 |
17 | Issues in Time-Series and Categorical Data Exploration | 229 |
18 | Visualizing the Simple Bayesian Classifier | 237 |
19 | Visualizing Data Mining Results with Domain Generalization Graphs | 251 |
20 | An Adaptive Interface Approach for Real-Time Data Exploration | 271 |
21 | Discovering New Relationships: A Brief Overview of Data Mining and Knowledge Discovery | 277 |
22 | A Taxonomy for Integrating Data Mining and Data Visualization | 291 |
23 | Integrating Data Mining and Visualization Processes | 299 |
24 | Multidimensional Education: Visual and Algorithmic Data Mining Domains and Symbiosis | 305 |
25 | Robust Beta Mining | 309 |
26 | Use of the Manifold Concept in Model Visualization | 313 |
27 | Data Warfare and Multidimensional Education | 315 |
28 | Document Mining and Visualization | 345 |
29 | Research Issues in the Analysis and Visualization of Massive Data Sets | 355 |
30 | Toward Smarter Databases: A Case-Building Toolkit | 361 |
31 | The NASD Regulation Advanced Detection System: Integrating Data Mining and Visualization for Break Detection in the NASDAQ Stock Market | 363 |
Index | 373 | |
About the Authors | 391 |
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