Overview
- Presents the latest findings in classification, statistical learning, and data analysis, including big data analytics and social networks
- Features a variety of applications in economics, environmental sciences, data management, and the pharmaceutical industry
- Focuses on methodological and computational aspects as well as on real-world problems
Part of the book series: Studies in Classification, Data Analysis, and Knowledge Organization (STUDIES CLASS)
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Table of contents (27 papers)
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Front Matter
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Exploratory data analysis
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Front Matter
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Clustering and Classification
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Front Matter
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Keywords
About this book
This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, heldin Santa Margherita di Pula (Cagliari), Italy, October 8–10, 2015.
Editors and Affiliations
About the editors
Francesco Mola is full professor of Statistics at the Department of Business and Economics at the University of Cagliari. He received his Ph.D in Computational Statistics and Data Analysis from the University of Naples Federico II. His research interests are in the field of multivariate data analysis and statistical learning, particularly data science and computational statistics. He has published more than sixty papers in international journals, encyclopedias, conference proceedings, and edited books.
Claudio Conversano is associate professor of Statistics at the Department of Business and Economics at the University of Cagliari. He received his Ph.D in Computational Statistics and Data Analysis from the University of Naples Federico II. His research interests include nonparametric statistics, statistical learning and computational finance. He has published more than forty papers in international journals, encyclopedias, conference proceedings, and edited books.
Maurizio Vichi is full professor of Statistics and head of the Department of Statistical Sciences at the Sapienza University of Rome. He is president of the Federation of European National Statistical Societies (FENStatS), former president of the Italian Statistical Society, and of the International Federation of Classification Societies (IFCS). He is coordinating editor of the journal Advances in Data Analysis and Classification, editor of the international book series Classification, Data Analysis and Knowledge Organization, and the series Studies in Theoretical and Applied Statistics, published by Springer. He is a member of ESAC
Bibliographic Information
Book Title: Classification, (Big) Data Analysis and Statistical Learning
Editors: Francesco Mola, Claudio Conversano, Maurizio Vichi
Series Title: Studies in Classification, Data Analysis, and Knowledge Organization
DOI: https://doi.org/10.1007/978-3-319-55708-3
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG 2018
Softcover ISBN: 978-3-319-55707-6Published: 22 February 2018
eBook ISBN: 978-3-319-55708-3Published: 21 February 2018
Series ISSN: 1431-8814
Series E-ISSN: 2198-3321
Edition Number: 1
Number of Pages: XVI, 242
Number of Illustrations: 44 b/w illustrations, 21 illustrations in colour
Topics: Statistical Theory and Methods, Statistics and Computing/Statistics Programs, Statistics for Business, Management, Economics, Finance, Insurance, Data Mining and Knowledge Discovery, Big Data