Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, 3rd Edition 2023

  • 20h 48m
  • Erez Shmueli, Lior Rokach, Oded Maimon
  • Springer
  • 2023

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.

About the Author

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.

In this Book

  • Data Science and Knowledge Discovery Using Machine Learning Methods
  • Handling Missing Attribute Values
  • Data Integration Process Automation Using Machine Learning—Issues and Solution
  • Rule Induction
  • Nearest-Neighbor Methods—A Modern Perspective
  • Support Vector Machines
  • Empowering Interpretable, Explainable Machine Learning Using Bayesian Network Classifiers
  • Soft Decision Trees
  • Quality Assessment and Evaluation Criteria in Supervised Learning
  • Trajectory Clustering Analysis
  • Clustering High-Dimensional Data
  • Fuzzy C-Means Clustering—Advances and Challenges (Part II)
  • Clustering in Streams
  • Introduction to Deep Learning
  • Graph Embedding
  • Autoencoders
  • Generative Adversarial Networks
  • Spatial Data Science
  • Multimedia Data Learning
  • Web Mining
  • Mining Temporal Data
  • Cloud Big Data Mining and Analytics—Bringing Greenness and Acceleration in the Cloud
  • Multi-Label Ranking—Mining Multi-Label and Label Ranking Data
  • Reinforcement Learning for Data Science
  • Adversarial Machine Learning
  • Ensembled Transferred Embeddings
  • Data Mining in Medicine
  • Recommender Systems
  • Activity Recognition
  • Social Network Analysis for Disinformation Detection
  • Online Propaganda Detection
  • Interpretable Machine Learning forFinancial Applications
  • Predictive Analytics for Targeting Decisions
  • Machine Learning for the Geosciences
  • Sentiment Analysis for Social Text
  • Human Resources-Based Organizational Data Mining (HRODM)—Themes, Trends, Focus, Future
  • Algorithmic Fairness
  • Privacy-Preserving Data Mining (PPDM)
  • Explainable Machine Learning and Visual Knowledge Discovery
  • Visual Analytics and Human Involvement in Machine Learning
  • Explainable Artificial Intelligence (XAI)—Motivation, Terminology, and Taxonomy