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. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018).

Chapter1: A Systematic Review on Supervised & Unsupervised Machine Learning Algorithms for Data Science.- Chapter2: Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints.- Chapter3: Distributed Single-Source Shortest Path Algorithms with Two Dimensional Graph Layout.- Chapter4: Using Non-Negative Tensor Decomposition for Unsupervised Textual Influence Modeling.- Chapter5: Survival Support Vector Machines: A Simulation Study and Its Health-related Application.- Chapter6: Semantic Unsupervised Learning for Word Sense Disambiguation.- Chapter7: Enhanced Tweet Hybrid Recommender System using Unsupervised Topic Modeling and Matrix Factorization based Neural Network.- Chapter8: New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering.