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Classification and Data Science in the Digital Age
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The contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education. The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification.

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Keywords

  • Classification
  • Clustering
  • data analysis
  • Data science
  • dimension reduction
  • functional data analysis
  • Machine learning
  • Mutlivariate Analysis
  • network analysis
  • statistical inference
  • statistical learning
  • thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
  • thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software
  • thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures
  • thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
  • thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
  • thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
  • Time Series Analysis

Links

DOI: 10.1007/978-3-031-09034-9

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