Supervised and Unsupervised Learning for Data Science

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.

Professor Michael W. Berry is a Full Professor in the Departments of Electrical Engineering and Computer Science (EECS) and Mathematics at the University of Tennessee, Knoxville. He served as Interim Department Head of Computer Science from January 2004 to June 2007, and as Associate Head in the Department of Electrical Engineering and Computer Science from July 2007 to July 2012. He worked in the Communications Product Division of IBM in Raleigh, NC for about 1 year before accepting a research staff position in the Center for Supercomputing Research and Development at the University of Illinois at Urbana-Champaign. In 1990, he received a PhD in Computer Science from the University of Illinois at Urbana-Champaign. Prof. Berry is the co-author of "Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods" (SIAM, 1994) and "Understanding Search Engines: Mathematical Modeling and Text Retrieval, Second Edition" (Bestseller, SIAM, 2005) and editor of "Computational Information Retrieval" (SIAM, 2001), "Survey of Text Mining: Clustering, Classification, and Retrieval" (Springer-Verlag, 2003, 2007), "Lecture Notes in Data Mining" (Bestseller, World Scientific, 2006), "Text Mining: Applications and Theory" (Wiley, 2010), and "High-Performance Scientific Computing" (Springer, 2012). He has published well over 150 peer-refereed journal and conference publications and book chapters. He has organized numerous workshops on Text Mining and was Conference Co-Chair of the 2003 SIAM Third International Conference on Data Mining (May 1-3) in San Francisco, CA. He was Program Co-Chair of the 2004 SIAM Fourth International Conference on Data Mining (April 22-24) in Orlando, FL., and he was a keynote speaker at the 2015 International Conference on Soft Computing in Data Science (SCDS2015). He was also honorary chair of the 2016 International Conference on Soft Computing in Data Science (SCDS2016) in Kuala Lumpur, Malaysia. His research interests include