Classification, (big) data analysis and statistical learning
(eBook)
Contributors:
Published:
Cham, Switzerland : Springer, 2018.
Format:
eBook
ISBN:
9783319557083, 3319557084, 9783319557090, 3319557092
Physical Desc:
1 online resource (xvii, 242 pages) : illustrations (some color)
Status:
Ebsco (CCU)
Description
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, held in Santa Margherita di Pula (Cagliari), Italy, October 8-10, 2015.
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Citations
APA Citation (style guide)
Mola, F., Conversano, C., & Vichi, M. (2018). Classification, (big) data analysis and statistical learning. Cham, Switzerland, Springer.
Chicago / Turabian - Author Date Citation (style guide)Mola, Francesco, Claudio, Conversano and Maurizio Vichi. 2018. Classification, (big) Data Analysis and Statistical Learning. Cham, Switzerland, Springer.
Chicago / Turabian - Humanities Citation (style guide)Mola, Francesco, Claudio, Conversano and Maurizio Vichi, Classification, (big) Data Analysis and Statistical Learning. Cham, Switzerland, Springer, 2018.
MLA Citation (style guide)Mola, Francesco,, et al. Classification, (big) Data Analysis and Statistical Learning. Cham, Switzerland, Springer, 2018.
Note! Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.
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Language:
English
UPC:
10.1007/978-3-319-55708-3
Notes
Description
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, held in Santa Margherita di Pula (Cagliari), Italy, October 8-10, 2015.
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Grouped Work ID:
50c31553-2f9f-a91e-a518-41b4a9688690
Record Information
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| 505 | 0 | |a Intro; Preface; Acknowledgements; Contents; Editors and Contributors; Big Data; From Big Data to Information: Statistical Issues Through a Case Study; 1 Introduction on Big Data; 2 Case Study; 2.1 ISTAT Dataset; 2.2 Telecom Italia Dataset; 2.3 Method of Comparison of the Two Datasets; 2.4 Results; 3 Conclusions; References; Enhancing Big Data Exploration with Faceted Browsing; 1 Introduction; 2 Faceted Browsing and Big Data; 3 Improving Faceted Navigation with Bayesian Networks; 4 Big Data and Solr; 5 Test; 6 Conclusion and Future Work; References. | |
| 505 | 8 | |a Big Data Meet Pharmaceutical Industry: An Application on Social Media Data1 Introduction; 2 The Goal and the Data; 3 Methodology and Results; 4 Further Developments; References; Electre Tri Machine Learning Approach to the Record Linkage; 1 Linked Data: The Record Linkage; 2 The Multiple Criteria Electre Tri Method: A Brief Description; 3 Application to Real Data: A Preliminary Stage; 4 Conclusions; References; Social Networks; Finite Sample Behavior of MLE in Network Autocorrelation Models; 1 Introduction; 2 A Brief Review of Network Autocorrelation Models; 3 Simulation Design; 4 Results. | |
| 505 | 8 | |a 5 Discussion and ConclusionsReferences; Network Analysis Methods for Classification of Roles; 1 Introduction; 2 Theoretical Framework; 3 Materials and Methods; 4 Results: The Classification of Social Roles; 5 Conclusions; References; MCA-Based Community Detection; 1 Community Detection Algorithms; 2 MCA Based Consensus Community Detection; 3 The Analysis of the Consensus Matrix; 4 Simulation Study; 5 Application on Real Data; 6 Conclusions; References; Exploratory Data Analysis; Rank Properties for Centred Three-Way Arrays; 1 Introduction; 2 Notation and Known Results; 3 Main Result. | |
| 505 | 8 | |a 4 Examples5 Conclusion; References; Principal Component Analysis of Complex Data and Application to Climatology; 1 Introduction; 2 Complex Singular Value Decomposition; 3 Data; 4 Results; 4.1 The Comparison of Methods; 4.2 The CPCA of Winds; 5 Conclusion; References; Motivations and Expectations of Students' Mobility Abroad: A Mapping Technique; 1 Introduction; 2 Questionnaire and Sample; 3 VOSviewer; 4 Results; 5 Conclusions; References; Testing Circular Antipodal Symmetry Through Data Depths; 1 Introduction; 2 Reflective and Antipodal Symmetry. | |
| 505 | 8 | |a 3 Data Depth-Based Tests for Antipodal Symmetry3.1 The Angular Simplicial and the Arc Distance Depths; 3.2 Tests for Antipodal Symmetry; 4 Evaluating the Test Procedures: An Empirical Study; 4.1 Simulation Design; 4.2 Simulation Results: Nominal Versus Observed Significance Level; 4.3 Simulation Results: Power of the Tests; 4.4 Simulation Results: Computational Costs; 5 Findings and Final Remarks; References; Statistical Modeling; Multivariate Stochastic Downscaling for Semicontinuous Data; 1 Introduction; 2 Joint Spatial Modeling; 2.1 Beyond the First Stage of Modeling. | |
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