Machine Learning for Data Science Handbook : Data Mining and Knowledge Discovery Handbook

Cover
Front Matter
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