Data Mining is a subject that covers fundamental concepts and algorithms, and the text requires only a modest background in mathematics.

Format: Paperback / softback
Length: 864 pages
Publication date: 31 May 2019
Publisher: Pearson Education Limited

Introduction to Data Mining

Data mining is a rapidly growing field that involves extracting valuable insights and patterns from large datasets. It has become an essential tool in various industries, such as finance, healthcare, marketing, and more. This introductory text aims to provide a comprehensive understanding of data mining concepts and algorithms for beginners.

The book begins by introducing the basic concepts of data mining, such as data representation, data preprocessing, and pattern recognition. It then delves into more advanced topics, such as machine learning, neural networks, and genetic algorithms. Each concept is explored thoroughly and supported with numerous examples to help readers understand how it can be applied in real-world scenarios.

One of the key strengths of this text is its accessibility. The author assumes only a modest background in mathematics, making it suitable for students with a variety of academic backgrounds. The major topics are organized into two chapters, each covering basic and advanced concepts. This allows readers to build a solid foundation in data mining before moving on to more complex topics.

In addition, the text includes practical examples and case studies from various industries to demonstrate the applications of data mining techniques. These examples help readers see how data mining can be used to solve real-world problems and make informed decisions.

Overall, Introduction to Data Mining is a valuable resource for anyone interested in learning data mining. Whether you are a student, professional, or simply curious about the field, this text will provide you with the knowledge and skills you need to get started.

Data Mining Concepts and Algorithms

Data mining is a process that involves extracting valuable insights and patterns from large datasets. It is a rapidly growing field that has become an essential tool in various industries, such as finance, healthcare, marketing, and more. This introductory text aims to provide a comprehensive understanding of data mining concepts and algorithms for beginners.

The book begins by introducing the basic concepts of data mining, such as data representation, data preprocessing, and pattern recognition. It then delves into more advanced topics, such as machine learning, neural networks, and genetic algorithms. Each concept is explored thoroughly and supported with numerous examples to help readers understand how it can be applied in real-world scenarios.

One of the key strengths of this text is its accessibility. The author assumes only a modest background in mathematics, making it suitable for students with a variety of academic backgrounds. The major topics are organized into two chapters, each covering basic and advanced concepts. This allows readers to build a solid foundation in data mining before moving on to more complex topics.

In addition, the text includes practical examples and case studies from various industries to demonstrate the applications of data mining techniques. These examples help readers see how data mining can be used to solve real-world problems and make informed decisions.

Overall, Introduction to Data Mining is a valuable resource for anyone interested in learning data mining. Whether you are a student, professional, or simply curious about the field, this text will provide you with the knowledge and skills you need to get started.

Weight: 1306g
Dimension: 191 x 234 x 27 (mm)
ISBN-13: 9780273769224
Edition number: 2 ed