This book brings together research articles by active practitioners and leading researchers reporting recent advances in the field of knowledge discovery. An overview of the field, looking at the issues and challenges involved is followed by coverage of recent trends in data mining. This provides the context for the subsequent chapters on methods and applications. Part I is devoted to the foundations of mining different types of complex data like trees, graphs, links and sequences. A knowledge discovery approach based on problem decomposition is also described. Part II presents important applications of advanced mining techniques to data in unconventional and complex domains, such as life sciences, world-wide web, image databases, cyber security and sensor networks. With a good balance of introductory material on the knowledge discovery process, advanced issues and state-of-the-art tools and techniques, this book will be useful to students at Masters and PhD level in Computer Science, as well as practitioners in the field.
Author(s): Ujjwal Maulik, Lawrence B. Holder, Diane J. Cook
Series: Advanced information and knowledge processing
Edition: 1
Publisher: Springer
Year: 2005
Language: English
Pages: 374
City: [New York]; [London]
Tags: Информатика и вычислительная техника;Искусственный интеллект;Интеллектуальный анализ данных;
Advanced Methods for Knowledge Discovery from Complex Data......Page 1
Contents......Page 6
Contributors......Page 8
Preface......Page 13
Part I -- Foundations......Page 17
1 - Knowledge Discovery and Data Mining......Page 18
2 - Automatic Discovery of Class Hierarchies via Output Space Decomposition......Page 58
3 - Graph-based Mining of Complex Data......Page 89
4 - Predictive Graph Mining with Kernel Methods......Page 108
5 - TreeMiner: An Efficient Algorithm for Mining Embedded Ordered Frequent Trees......Page 135
6 - Sequence Data Mining......Page 164
7 - Link-based Classification......Page 199
Part II -- Applications......Page 218
8 - Knowledge Discovery from Evolutionary Trees......Page 219
9 - Ontology-Assisted Mining of RDF Documents......Page 239
10 - Image Retrieval using Visual Features and Relevance Feedback......Page 261
11 - Significant Feature Selection Using Computational Intelligent Techniques for Intrusion Detection......Page 292
12 - On-board Mining of Data Streams in Sensor Networks......Page 314
13 - Discovering an Evolutionary Classifier over a High-speed Nonstatic Stream......Page 343
Index......Page 370
