| Advanced Methods for Knowledge Discovery from Complex Data 2005 Edition Contributor(s): Maulik, Ujjwal (Editor), Holder, Lawrence B. (Editor), Cook, Diane J. (Editor) |
|||
 |
ISBN: 1852339896 ISBN-13: 9781852339890 Publisher: Springer
Binding Type: Hardcover - See All Available Formats & Editions Published: November 2005 Annotation: 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. Click for more in this series: Advanced Information and Knowledge Processing |
||
| Additional Information |
| BISAC Categories: - Computers | Computer Science - Computers | Databases - General - Computers | System Administration - Storage & Retrieval |
| Dewey: 005.1 |
| Series: Advanced Information and Knowledge Processing |
| Physical Information: 0.94" H x 6.32" W x 9.54" L (1.53 lbs) 369 pages |
| Features: Bibliography, Illustrated, Index, Table of Contents |
| Descriptions, Reviews, Etc. |
| Publisher Description: The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit, therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters |
| Customer ReviewsSubmit your own review |
| To tell a friend about this book, you must Sign In First! |
Copyright © 2024 TheReadingWarehouse.com