Data Compression for Data Mining Algorithms

Data Compression for Data Mining Algorithms tackles the important problems in the design of more efficient data mining algorithms by way of data compression techniques and provides the first systematic and comprehensive description of the relationships between data compression mechanisms and the computations involved in data mining algorithms. Data mining algorithms are powerful analytical techniques used across various disciplines, including business, engineering, and science. However, in the big data era, tasks such as association rule mining and classification often require multiple scans of databases, while clustering and outlier detection methods typically depend on Euclidean distance for similarity measures, leading to high computational costs.

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

Dr. Xiaochun Wang received her BS degree from Beijing University and her MS degree in data compression and PhD degree in mobile robotics from the Department of Electrical Engineering and Computer Science at Vanderbilt University. She was an associate professor at the School of Software Engineering at Xi’an Jiaotong University and taught Database Management and Data Mining courses from 2010 to 2021. She currently works as a senior scientist at Xi’an Tuowei Hi-Tech Corporation. Her research interests include data mining, pattern recognition, signal processing, and computer vision.