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Practical Applications of Data Minining
Practical Applications of Data Mining emphasizes both theory and applications of data mining algorithms. Various topics of data mining techniques are identified and described throughout, including clustering, association rules, rough set theory, probability theory, neural networks, classification, and fuzzy logic. Each of these techniques is explored with a theoretical introduction and its effectiveness is demonstrated with various chapter examples. This book will help any database and IT professional understand how to apply data mining techniques to real-world problems.
Following an introduction to data mining principles, Practical Applications of Data Mining introduces association rules to describe the generation of rules as the first step in data mining. It covers classification and clustering methods to show how data can be classified to retrieve information from data. Statistical functions and rough set theory are discussed to demonstrate how statistical and rough set formulas can be used for data analytics and knowledge discovery. Neural networks is an important branch in computational intelligence. It is introduced and explored in the text to investigate the role of neural network algorithms in data analytics.
Features & Benefits
- Offers an introduction to practical applications of data mining algorithms with clear illustrations of concepts and techniques
- Contains a rich set of examples in each chapter to connect theories to practices
- Covers topics needed to meet the requirements of modern data and knowledge engineering processes
- Enhances student learning with online access to data mining algorithm implementation
- Chapter 01: Introduction to Data Mining
- Chapter 02: Association Rules
- Chapter 03: Classification Learning
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