Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case, the need for literature that addresses this is self-evident. New publications are appearing, covering the need for information from all fields of science and engineering, thanks to the universal relevance of data analysis and statistics packages.
This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis. The chapters included in this volume represent a cross-section of current concerns and research interests in these scientific areas. The material is divided into two parts: Computational Data Analysis, and Classification Data Analysis, with methods for both - providing the reader with both theoretical and applied information on data analysis methods, models and techniques and appropriate applications.
Part 1. Computational Data Analysis and Methods
1. Semi-supervised Learning Based on Distributionally Robust Optimization, Jose Blanchet and Yang Kang.
2. Updating of PageRank in Evolving Treegraphs, Benard Abola, Pitos Seleka Biganda, Christopher Engstörm, John Magero Mango, Godwin Kakuba and Sergei Silvestrov.
3. Exploring The Relationship Between Ordinary PageRank, Lazy PageRank and Random Walk with Backstep PageRank for Different Graph Structures, Pitos Seleka Biganda, Benard Abola, Christopher Engstörm, John Magero Mango, Godwin Kakuba and Sergei Silvestrov.
4. On the Behavior of Alternative Splitting Criteria for CUB Model-based Trees, Carmela Cappelli, Rosaria Simone and Francesca Di Iorio.
5. Investigation on Life Satisfaction Through (Stratified) Chain Regression Graph Models, Federica Nicolussi and Manuela Cazzaro.
Part 2. Classification Data Analysis and Methods
6. Selection of Proximity Measures for a Topological Correspondence Analysis, Rafik Abdelssam.
7. Support Vector Machines: A Review and Applications in Statistical Process Monitoring, Anastasios Apsemidis and Stelios Psarakis.
8. Binary Classification Techniques: An Application on Simulated and Real Bio-medical Data, Fragkiskos G. Bersimis, Iraklis Varlamis, Malvina Vamvakari and Demosthenes B. Panagiotakos.
9. Some Properties of the Multivariate Generalized Hyperbolic Models, Stergios B. Fotopoulos, Venkata K. Jandhyala and Alex Paparas.
10. On Determining the Value of Online Customer Satisfaction Ratings – A Case-based Appraisal, Jim Freeman.
11. Projection Clustering Unfolding: A New Algorithm for Clustering Individuals or Items in a Preference Matrix, Mariangela Sciandra, Antonio D’Ambrosio and Antonella Plaia.
Andreas Makrides is Associate Lecturer of Statistics at the University of Central Lancashire, Cyprus (UClan) and conducted postdoctoral research at the Laboratoire de Mathématiques Raphaël Salem, Université de Rouen, France.
Alex Karagrigoriou is Professor of Probability and Statistics at the University of the Aegean, Greece. He is also the faculty’s Head of Graduate Studies and Director of the in-house Laboratory of Statistics and Data Analysis.
Christos H. Skiadas is former vice-Rector at the Technical University of Crete, Greece and founder of its Data Analysis and Forecasting Laboratory. He continues his research in ManLab, in the faculty’s Department of Production Engineering and Management.
Table of Contents
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