Overview
- Updated version of previous editions of Data Mining Knowledge Discovery Handbook
- Data science and machine learning major concepts, challenges are presented
- Offers a comprehensive, yet concise reference source for researchers, students and practitioners
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Table of contents (41 chapters)
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Front Matter
Keywords
- Data Science
- Analytics
- Machine Learning
- Deep Learning
- Data Mining
- Knowledge Discovery
- Ensemble Learning
- Clustering
- Neural Networks
- decision trees
- Web Mining
- Text Mining
- Privacy Preserving
- Artificial Intelligence
- Generative adversarial networks
- autoencoder
- ensemble learning
- recommender system
- dimensionality reduction
- data imputation
About this book
This book is a major update to the very successful first and second editions (2005 and 2010) of Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications and aspects are introduced. The new title of this handbook and its content reflect these changes thoroughly. Some existing chapters have been brought up to date. In addition to major revision of the existing chapters, the new edition includes totally new topics, such as: deep learning, explainable AI, human factors and social issues and advanced methods for big-data. The significant enhancement to the content reflects the growth in importance of data science. The third edition is also a timely opportunity to incorporate many other changes based on peers and students’ feedback.
This comprehensive handbook also presents a coherent and unified repository of data science major concepts, theories, methods, trends, challenges and applications. It covers all the crucial important machine learning methods used in data science.
Today's accessibility and abundance of data make data science matters of considerable importance and necessity. Given the field's recent growth, it's not surprising that researchers and practitioners now have a wide range of methods and tools at their disposal. While statistics is fundamental for data science, methods originated from artificial intelligence, particularly machine learning, are also playing a significant role.
This handbook aims to serve as the main reference for researchers in the fields of information technology, e-Commerce, information retrieval, data science, machine learning, data mining, databases and statistics as well as advanced level students studying computer science or electrical engineering. Practitioners working within these related fields and data scientists will also want to purchase this handbook as a reference.
Editors and Affiliations
About the editors
Prof. Oded Maimon is the Oracle chaired Professor at Tel-Aviv University, Previously at MIT. Oded is a leader expert in the field of data mining and knowledge discovery. He published many articles on new algorithms and seven significant award winning books in the field since 2000. He has also developed and implemented successful applications in the Industry. He heads an international research group sponsored by European Union awards.
Dr. Lior Rokach is a senior lecturer at the Department of Information System Engineering at Ben-Gurion University. He is a recognized expert in intelligent information systems and has held several leading positions in this field. His main areas of interest are Data Mining, Pattern Recognition, and Recommender Systems. Dr. Rokach is the author of over 70 refereed papers in leading journals, conference proceedings and book chapters. In addition he has authored six books and edited three others books.
Bibliographic Information
Book Title: Machine Learning for Data Science Handbook
Book Subtitle: Data Mining and Knowledge Discovery Handbook
Editors: Lior Rokach, Oded Maimon, Erez Shmueli
DOI: https://doi.org/10.1007/978-3-031-24628-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-24627-2Published: 18 August 2023
Softcover ISBN: 978-3-031-24630-2Due: 25 September 2023
eBook ISBN: 978-3-031-24628-9Published: 17 August 2023
Edition Number: 3
Number of Pages: VII, 985
Number of Illustrations: 58 b/w illustrations, 156 illustrations in colour
Topics: Machine Learning, Artificial Intelligence, Data Mining and Knowledge Discovery, Information Storage and Retrieval