Rokach L., Maimon O., Shmueli E. (eds.) Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook

3rd edition. — Springer, 2023. — 975 p. — ISBN: 978-3-031-24627-2.This book is a major update to the very successful first and second editions (2005 and 2010) of the 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 a major revision of the existing chapters, the new edition includes 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 the 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 originating from artificial intelligence, particularly machine learning, are also playing a significant role. 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 originating from artificial intelligence, particularly machine learning, are also playing a significant role.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 for Financial Applications.