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.
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3 of 3 people found the following review helpful:
4.0 out of 5 stars
A Great text for Data Mining Course,
By William Wong (Canada) - See all my reviews
This review is from: Data Mining Methods for Knowledge Discovery (The Springer International Series in Engineering and Computer Science) (Hardcover)
Cover different methods for Data Mining. They include: Rough set, fuzzy set, Genetic Alg, Neural Net, Clustering .... You will find this text useful if you are new to Data Mining. Do not expect further details in each fields. In general, it was a great text for a data mining course at graduate level.
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Inside This Book
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First Sentence:
Is data mining a totally new research pursuit? Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
quanta matrix, summation layer neuron, aggregating layer, interclass separability, image recognition neural network, dynamic reducts, feature selection criterion, limited size data, feature selection criteria, discernibility matrix, information granularity, sensory layer, winning neuron, optimal feature selection, fuzzy production rules, output activation function, fuzzy context, partial star, inductive machine learning, labeled patterns, partial supervision, genetic computing, rough sets, selected feature subset, inconsistency count Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, International Workshop, John Wiley, Neural Computation, San Jose, Color Windows, Object Condition, Cardiology Conf, Computational Intelligence, Computer Society Press, Criteria Based, Information Sciences, Tree Rules, Object Attributes, Oxford Univ, United States New!
Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Is data mining a totally new research pursuit? Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
quanta matrix, summation layer neuron, aggregating layer, interclass separability, image recognition neural network, dynamic reducts, feature selection criterion, limited size data, feature selection criteria, discernibility matrix, information granularity, sensory layer, winning neuron, optimal feature selection, fuzzy production rules, output activation function, fuzzy context, partial star, inductive machine learning, labeled patterns, partial supervision, genetic computing, rough sets, selected feature subset, inconsistency count Key Phrases - Capitalized Phrases (CAPs): (learn more)
Morgan Kaufmann, International Workshop, John Wiley, Neural Computation, San Jose, Color Windows, Object Condition, Cardiology Conf, Computational Intelligence, Computer Society Press, Criteria Based, Information Sciences, Tree Rules, Object Attributes, Oxford Univ, United States New!
Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 63
books:
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26
books
cite this book:
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- Knowledge Discovery in Databases (Aaai Press Copublications) by Gregory Piatetsky-Shapiro on 5 pages
- Machine Learning by Tom M. Mitchell on 5 pages
- Rough Sets by Lech Polkowski on page 67, and page 68
- Data Mining by Jiawei Han on page 6, and page 21
- Machine Learning: An Artificial Intelligence Approach (Volume I) by Ryszard S. Michalski on page 24, and page 288
See all 63 books this book cites
- New Learning Paradigms in Soft Computing (Studies in Fuzziness and Soft Computing) by Lakhmi C. Jain on page 310, and page 322
- Intelligent Data Warehousing: From Data Preparation to Data Mining by Zhengxin Chen on page 8, and page 206
- Intelligent Information Processing and Web Mining: Proceedings of the International IIS: IIPWM'03 Conference held in Zakopane, Poland, June 2-5, 2003 (Advances in Intelligent and Soft Computing) by Mieczyslaw A. Klopotek on page 232
- Intelligent Technologies for Information Analysis by Ning Zhong on page 507
- Data Mining and Uncertain Reasoning: An Integrated Approach by Zhengxin Chen in Back Matter
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