Data Mining Methods for Knowledge Discovery

✍ Scribed by Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski (auth.)

Publisher: Springer US
Year: 1998
Tongue: English
Leaves: 507
Series: The Springer International Series in Engineering and Computer Science 458
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography.
Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.

✦ Table of Contents

Front Matter....Pages i-xxi
Data Mining and Knowledge Discovery....Pages 1-26
Rough Sets....Pages 27-71
Fuzzy Sets....Pages 73-129
Bayesian Methods....Pages 131-191
Evolutionary Computing....Pages 193-227
Machine Learning....Pages 229-308
Neural Networks....Pages 309-374
Clustering....Pages 375-429
Preprocessing....Pages 431-489
Back Matter....Pages 491-495

✦ Subjects

Data Structures, Cryptology and Information Theory; Information Storage and Retrieval; Artificial Intelligence (incl. Robotics); Business Information Systems

📜 SIMILAR VOLUMES

Mathematical Methods for Knowledge Disco

📁 Mathematical Methods for Knowledge Discovery and Data Mining

✍ Giovanni Felici, Carlo Vercellis 📂 Library 📅 2007 🌐 English

The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques

Mathematical methods for knowledge disco

📁 Mathematical methods for knowledge discovery and data mining

✍ Giovanni Felici, Giovanni Felici, Carlo Vercellis 📂 Library 📅 2008 🏛 Information Science Reference 🌐 English

Mathematical Methods for Knowledge Disco

📁 Mathematical Methods for Knowledge Discovery and Data Mining

✍ Giovanni Felici, Giovanni Felici, Carlo Vercellis 📂 Library 📅 2008 🏛 Information Science Reference 🌐 English

Data Mining and Knowledge Discovery for

📁 Data Mining and Knowledge Discovery for Geoscientists

✍ Guangren Shi (Auth.) 📂 Library 📅 2014 🏛 Elsevier 🌐 English

<p>Currently there are major challenges in data mining applications in the geosciences. This is due primarily to the fact that there is a wealth of available mining data amid an absence of the knowledge and expertise necessary to analyze and accurately interpret the same data. Most geoscientists hav

Data mining and knowledge discovery for

📁 Data mining and knowledge discovery for geoscientists

✍ Shi, Guangren 📂 Library 📅 2014 🏛 Elsevier 🌐 English

1. Introduction -- 2. Probability and statistics -- 3. Artificial neural networks -- 4. Support vector machines -- 5. Decision trees -- 6. Bayesian classification -- 7. Cluster analysis -- 8. Kriging -- 9. Other soft computing algorithms for geosciences -- 10. A practical software system of data min

Soft Computing for Knowledge Discovery a

📁 Soft Computing for Knowledge Discovery and Data Mining

✍ Oded Maimon, Lior Rokach 📂 Library 📅 2007 🏛 Springer 🌐 English

Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the env