Supervised and Unsupervised Learning for Data Science

✍ Scribed by Michael W. Berry, Azlinah Mohamed, Bee Wah Yap

• Publisher: Springer International Publishing
• Year: 2020
• Tongue: English
• Leaves: 191
• Series: Unsupervised and Semi-Supervised Learning
• Edition: 1st ed. 2020
• Category: Library

✦ Synopsis

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).

• Includes new advances in clustering and classification using semi-supervised and unsupervised learning;
• Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning;
• Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

✦ Table of Contents

Front Matter ....Pages i-viii
Front Matter ....Pages 1-1
A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science (Mohamed Alloghani, Dhiya Al-Jumeily, Jamila Mustafina, Abir Hussain, Ahmed J. Aljaaf)....Pages 3-21
Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints (Chidchanok Lursinsap)....Pages 23-37
Distributed Single-Source Shortest Path Algorithms with Two-Dimensional Graph Layout (Thap Panitanarak)....Pages 39-58
Using Non-negative Tensor Decomposition for Unsupervised Textual Influence Modeling (Robert E. Lowe, Michael W. Berry)....Pages 59-82
Front Matter ....Pages 83-83
Survival Support Vector Machines: A Simulation Study and Its Health-Related Application (Dedy Dwi Prastyo, Halwa Annisa Khoiri, Santi Wulan Purnami, Suhartono, Soo-Fen Fam, Novri Suhermi)....Pages 85-100
Semantic Unsupervised Learning for Word Sense Disambiguation (Dian I. Martin, Michael W. Berry, John C. Martin)....Pages 101-120
Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network (Arisara Pornwattanavichai, Prawpan Brahmasakha na sakolnagara, Pongsakorn Jirachanchaisiri, Janekhwan Kitsupapaisan, Saranya Maneeroj)....Pages 121-143
New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering (Ameer A. Jebur, Dhiya Al-Jumeily, Khalid R. Aljanabi, Rafid M. Al Khaddar, William Atherton, Zeinab I. Alattar et al.)....Pages 145-182
Back Matter ....Pages 183-187

✦ Subjects

Engineering; Communications Engineering, Networks; Signal, Image and Speech Processing; Pattern Recognition; Data Mining and Knowledge Discovery