Data Compression for Data Mining Algorithms
Language: English
Published by Elsevier Science, 2026
- Softcover
- New
Condition: New
£ 165.12
Quantity: 2 available
Add to basketItem description from seller
Covers popular data compression methods and their solutions to aid in the development and application of data mining algorithmsIncludes projects and problems solved with Python to help readers create programs for both data compression and d.
Seller Inventory # 2769883082
- Title
- Data Compression for Data Mining Algorithms
- Author
- Xiaochun Wang
- Publisher
- Elsevier Science
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0443405417
- ISBN 13
- 9780443405419
Data Compression for Data Mining Algorithms addresses these challenges by focusing on the scalarization of data mining algorithms, leveraging data compression techniques to reduce dataset sizes and applying information theory principles to minimize computations involved in tasks such as feature selection and similarity computation. The book features the latest developments in both lossless and lossy data compression methods and provides a comprehensive exposition of data compression methods for data mining algorithm design from multiple points of view.
Key discussions include Huffman coding, scalar and vector quantization, transforms, subbands, wavelet-based compression for scalable algorithms, and the role of neural networks, particularly deep learning, in feature selection and dimensionality reduction. The book’s contents are well-balanced for both theoretical analysis and real-world applications, and the chapters are well organized to compose a solid overview of the data compression techniques for data mining. To provide the reader with a more complete understanding of the material, projects and problems solved with Python are interspersed throughout the text.
- Covers popular data compression methods and their solutions to aid in the development and application of data mining algorithms
- Includes projects and problems solved with Python to help readers create programs for both data compression and data mining problems
- Focuses on the scalarization of data mining algorithms, leveraging data compression techniques to reduce dataset sizes and applying information theory principles to minimize computations
- Simplifies the content of the field of data compression by covering topics that are widely useful from a data mining perspective
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About the Author
"About the title" may belong to another edition of this title.
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