Recent Trends and Future Challenges in Leaing from Data (Studies in Classification, Data Analysis, and Knowledge Organization)

by: Cristina Davino (Editor),Francesco Palumbo (Editor),Adalbert F. X. Wilhelm (Editor),Hans A. Kestler (Editor)&1more

Publisher: Springer

Edition: 2024th

Publication Date: 2024/8/10

Language: English

Print Length: 163 pages

ISBN-10: 3031544676

ISBN-13: 9783031544675

Book Description

This book collects together selected peer-reviewed contributions presented at the European Conference on Data Analysis, ECDA 2022, held in Naples, Italy, September 14-16, 2022. Highlighting the role of statistics in discovering novel and interesting pattes in the era of big data, it follows the motto of the conference:“Avoiding drowning in the data:recent trends and future challenges in leaing from data”. The central focus is on multidisciplinary approaches to data analysis, classification, and the interface between computer science, data mining and statistics. Both methodological and applied topics are covered. The former includes supervised and unsupervised techniques with particular emphasis on advances in regression and clustering analysis and constructing composite indicators. The applications are mainly in risk analysis, biology, and education. The volume is organized into four main macro themes:methodological contributions in the social sciences and education, multivariate analysis methods for big data, innovative contributions for applications inspired by biology, and strategies for analyzing complex data in finance.

About the Author

This book collects together selected peer-reviewed contributions presented at the European Conference on Data Analysis, ECDA 2022, held in Naples, Italy, September 14-16, 2022. Highlighting the role of statistics in discovering novel and interesting pattes in the era of big data, it follows the motto of the conference:“Avoiding drowning in the data:recent trends and future challenges in leaing from data”. The central focus is on multidisciplinary approaches to data analysis, classification, and the interface between computer science, data mining and statistics. Both methodological and applied topics are covered. The former includes supervised and unsupervised techniques with particular emphasis on advances in regression and clustering analysis and constructing composite indicators. The applications are mainly in risk analysis, biology, and education. The volume is organized into four main macro themes:methodological contributions in the social sciences and education, multivariate analysis methods for big data, innovative contributions for applications inspired by biology, and strategies for analyzing complex data in finance.

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