Berry M.W., Mohamed A.H, Yap B.W. (Eds.) Supervised and Unsupervised Learning for Data Science

Springer, 2020. — 191 p. — (Unsupervised and Semi-Supervised Learning). — ISBN: 978-3-030-22474-5.This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field.A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science
Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints
Distributed Single-Source Shortest Path Algorithms with Two-Dimensional Graph Layout
Using Non-negative Tensor Decomposition for Unsupervised Textual Influence Modeling
Survival Support Vector Machines: A Simulation Study and Its Health-Related Application
Semantic Unsupervised Learning for Word Sense Disambiguation
Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network
New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering