國立虎尾科技大學 |

Classification and data science in the digital age

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Classification and data science in the digital age/ edited by Paula Brito ... [et al.].
其他作者:	Brito, Paula.
團體作者:	International Federation of Classification Societies.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xiv, 416 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Statistical Software. -
電子資源:	https://doi.org/10.1007/978-3-031-09034-9
ISBN:	9783031090349

Classification and data science in the digital age
International Federation of Classification Societies.ConferencePorto, Portugal)2022 :

Classification and data science in the digital age[electronic resource] /edited by Paula Brito ... [et al.]. - Cham :Springer International Publishing :2023. - xiv, 416 p. :ill., digital ;24 cm. - Studies in classification, data analysis, and knowledge organization,2198-3321. - Studies in classification, data analysis, and knowledge organization..

Preface -- R. Abdesselam: A Topological Clustering of Individuals -- C. Anton and I. Smith: Model Based Clustering of Functional Data with Mild Outliers -- F. Antonazzo and S. Ingrassia: A Trivariate Geometric Classification of Decision Boundaries for Mixtures of Regressions -- E. Arnone, E. Cunial, and L. M. Sangalli: Generalized Spatio-temporal Regression with PDE Penalization -- R. Ascari and S. Migliorati: A New Regression Model for the Analysis of Microbiome Data -- R. Aschenbruck, G. Szepannek, and A. F. X. Wilhelm: Stability of Mixed-type Cluster Partitions for Determination of the Number of Clusters -- A. Ashofteh and P. Campos: A Review on Official Survey Item Classification for Mixed-Mode Effects Adjustment -- V. Batagelj: Clustering and Blockmodeling Temporal Networks - Two Indirect Approaches -- R. Boutalbi, L. Labiod, and M. Nadif: Latent Block Regression Model -- N. Chabane, M. Achraf Bouaoune, R. Amir Sofiane Tighilt, B. Mazoure, N. Tahiri, and V. Makarenkov: Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations -- T. Chadjipadelis and S. Magopoulou: COVID-19 Pandemic: a Methodological Model for the Analysis of Government's Preventing Measures and Health Data Records -- J. Champagne Gareau, É. Beaudry, and V. Makarenkov: pcTVI: Parallel MDP Solver Using a Decomposition into Independent Chains -- C. Di Nuzzo and S. Ingrassia: Three-way Spectral Clustering -- J. Dobša and H. A. L. Kiers: Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space -- J. Gama: Trends in Data Stream Mining -- L. A. García-Escudero, A. Mayo-Iscar, G. Morelli, and M. Riani: Old and New Constraints in Model Based Clustering -- V. G Genova, G. Giordano, G. Ragozini, and M. Prosperina Vitale: Clustering Student Mobility Data in 3-way Networks -- R. Giubilei: Clustering Brain Connectomes Through a Density-peak Approach -- T. Górecki, M. Šuczak, and P. Piasecki: Similarity Forest for Time Series Classification -- K. Hayashi, E. Hoshino, M. Suzuki, E. Nakanishi, K. Sakai, and M. Obatake: Detection of the Biliary Atresia Using Deep Convolutional Neural Networks Based on Statistical Learning Weights via Optimal Similarity and Resampling Methods -- Ch. Hennig: Some Issues in Robust Clustering -- J. Kalina and P. Janá£ek: Robustness Aspects of Optimized Centroids -- L. Labiod and M. Nadif: Data Clustering and Representation Learning Based on Networked Data -- Lazhar Labiod and Mohamed Nadif: Towards a Bi-stochastic Matrix Approximation of k-means and Some Variants -- A. LaLonde, T. Love, D. R. Young, and T. Wu: Clustering Adolescent Female Physical Activity Levels with an Infinite Mixture Model on Random Effects -- Á. López-Oriona, J. A. Vilar, and P. D'Urso: Unsupervised Classification of Categorical Time Series Through Innovative Distances -- D. Masís, E. Segura, J. Trejos, and A. Xavier: Fuzzy Clustering by Hyperbolic Smoothing -- R. Meng, H. K. H. Lee, and K. Bouchard: Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks -- H. Duy Nguyen, F. Forbes, G. Fort, and O. Cappé: An Online Minorization-Maximization Algorithm -- L. Palazzo and R. Ievoli: Detecting Differences in Italian Regional Health Services During Two Covid-19 Waves -- G. Panagiotidou and T. Chadjipadelis: Political and Religion Attitudes in Greece: Behavioral Discourses -- K. Pawlasová, I. Karafiátová, and J. Dvořák: Supervised Classification via Neural Networks for Replicated Point Patterns -- G. Perrone and G. Soffritti: Parsimonious Mixtures of Seemingly Unrelated Contaminated Normal Regression Models -- N. Pronello, R. Ignaccolo, L. Ippoliti, and S. Fontanella: Penalized Model-based Functional Clustering: a Regularization Approach via Shrinkage Methods -- D. Rodrigues, L. P. Reis, and B. M. Faria: Emotion Classification Based on Single Electrode Brain Data: Applications for Assistive Technology -- R. Scimone, A. Menafoglio, L. M. Sangalli, and P. Secchi: The Death Process in Italy Before and During the Covid-19 Pandemic: a Functional Compositional Approach -- O. Silva, Á. Sousa, and H. Bacelar-Nicolau: Clustering Validation in the Context of Hierarchical Cluster Analysis: an Empirical Study -- C. Silvestre, M. G. M. S. Cardoso, and M. Figueiredo: An MML Embedded Approach for Estimating the Number of Clusters -- Á. Sousa, O. Silva, M. Graça Batista, S. Cabral, and H. Bacelar-Nicolau: Typology of Motivation Factors for Employees in the Banking Sector: An Empirical Study Using Multivariate Data Analysis Methods -- J. Michael Spoor, J. Weber, and J. Ovtcharova: A Proposal for Formalization and Definition of Anomalies in Dynamical Systems -- N. Tahiri and A. Koshkarov: New Metrics for Classifying Phylogenetic Trees Using -means and the Symmetric Difference Metric -- S. D. Tomarchio: On Parsimonious Modelling via Matrix-variate t Mixtures -- G. Zammarchi, M. Romano, and C. Conversano: Evolution of Media Coverage on Climate Change and Environmental Awareness: an Analysis of Tweets from UK and US Newspapers.

Open access.

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.

ISBN: 9783031090349

Standard No.: 10.1007/978-3-031-09034-9doiSubjects--Topical Terms:

1390759
Statistical Software.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Classification and data science in the digital age
LDR:07419nam a2200349 a 4500 001 1120439
003 DE-He213
005 20231207064046.0
006 m d
007 cr nn 008maaau
008 240612s2023 sz s 0 eng d
020 $a 9783031090349 $q (electronic bk.)
020 $a 9783031090332 $q (paper)
024 7 $a 10.1007/978-3-031-09034-9 $2 doi
035 $a 978-3-031-09034-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45
072 7 $a UN $2 bicssc
072 7 $a COM031000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b I61 2022
110 2 $a International Federation of Classification Societies. $b Conference $n (17th : $d 2022 : $c Porto, Portugal) $3 1435722
245 1 0 $a Classification and data science in the digital age $h [electronic resource] / $c edited by Paula Brito ... [et al.].
260 $a Cham : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
300 $a xiv, 416 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in classification, data analysis, and knowledge organization, $x 2198-3321
505 0 $a Preface -- R. Abdesselam: A Topological Clustering of Individuals -- C. Anton and I. Smith: Model Based Clustering of Functional Data with Mild Outliers -- F. Antonazzo and S. Ingrassia: A Trivariate Geometric Classification of Decision Boundaries for Mixtures of Regressions -- E. Arnone, E. Cunial, and L. M. Sangalli: Generalized Spatio-temporal Regression with PDE Penalization -- R. Ascari and S. Migliorati: A New Regression Model for the Analysis of Microbiome Data -- R. Aschenbruck, G. Szepannek, and A. F. X. Wilhelm: Stability of Mixed-type Cluster Partitions for Determination of the Number of Clusters -- A. Ashofteh and P. Campos: A Review on Official Survey Item Classification for Mixed-Mode Effects Adjustment -- V. Batagelj: Clustering and Blockmodeling Temporal Networks - Two Indirect Approaches -- R. Boutalbi, L. Labiod, and M. Nadif: Latent Block Regression Model -- N. Chabane, M. Achraf Bouaoune, R. Amir Sofiane Tighilt, B. Mazoure, N. Tahiri, and V. Makarenkov: Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations -- T. Chadjipadelis and S. Magopoulou: COVID-19 Pandemic: a Methodological Model for the Analysis of Government's Preventing Measures and Health Data Records -- J. Champagne Gareau, É. Beaudry, and V. Makarenkov: pcTVI: Parallel MDP Solver Using a Decomposition into Independent Chains -- C. Di Nuzzo and S. Ingrassia: Three-way Spectral Clustering -- J. Dobša and H. A. L. Kiers: Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space -- J. Gama: Trends in Data Stream Mining -- L. A. García-Escudero, A. Mayo-Iscar, G. Morelli, and M. Riani: Old and New Constraints in Model Based Clustering -- V. G Genova, G. Giordano, G. Ragozini, and M. Prosperina Vitale: Clustering Student Mobility Data in 3-way Networks -- R. Giubilei: Clustering Brain Connectomes Through a Density-peak Approach -- T. Górecki, M. Šuczak, and P. Piasecki: Similarity Forest for Time Series Classification -- K. Hayashi, E. Hoshino, M. Suzuki, E. Nakanishi, K. Sakai, and M. Obatake: Detection of the Biliary Atresia Using Deep Convolutional Neural Networks Based on Statistical Learning Weights via Optimal Similarity and Resampling Methods -- Ch. Hennig: Some Issues in Robust Clustering -- J. Kalina and P. Janá£ek: Robustness Aspects of Optimized Centroids -- L. Labiod and M. Nadif: Data Clustering and Representation Learning Based on Networked Data -- Lazhar Labiod and Mohamed Nadif: Towards a Bi-stochastic Matrix Approximation of k-means and Some Variants -- A. LaLonde, T. Love, D. R. Young, and T. Wu: Clustering Adolescent Female Physical Activity Levels with an Infinite Mixture Model on Random Effects -- Á. López-Oriona, J. A. Vilar, and P. D'Urso: Unsupervised Classification of Categorical Time Series Through Innovative Distances -- D. Masís, E. Segura, J. Trejos, and A. Xavier: Fuzzy Clustering by Hyperbolic Smoothing -- R. Meng, H. K. H. Lee, and K. Bouchard: Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks -- H. Duy Nguyen, F. Forbes, G. Fort, and O. Cappé: An Online Minorization-Maximization Algorithm -- L. Palazzo and R. Ievoli: Detecting Differences in Italian Regional Health Services During Two Covid-19 Waves -- G. Panagiotidou and T. Chadjipadelis: Political and Religion Attitudes in Greece: Behavioral Discourses -- K. Pawlasová, I. Karafiátová, and J. Dvořák: Supervised Classification via Neural Networks for Replicated Point Patterns -- G. Perrone and G. Soffritti: Parsimonious Mixtures of Seemingly Unrelated Contaminated Normal Regression Models -- N. Pronello, R. Ignaccolo, L. Ippoliti, and S. Fontanella: Penalized Model-based Functional Clustering: a Regularization Approach via Shrinkage Methods -- D. Rodrigues, L. P. Reis, and B. M. Faria: Emotion Classification Based on Single Electrode Brain Data: Applications for Assistive Technology -- R. Scimone, A. Menafoglio, L. M. Sangalli, and P. Secchi: The Death Process in Italy Before and During the Covid-19 Pandemic: a Functional Compositional Approach -- O. Silva, Á. Sousa, and H. Bacelar-Nicolau: Clustering Validation in the Context of Hierarchical Cluster Analysis: an Empirical Study -- C. Silvestre, M. G. M. S. Cardoso, and M. Figueiredo: An MML Embedded Approach for Estimating the Number of Clusters -- Á. Sousa, O. Silva, M. Graça Batista, S. Cabral, and H. Bacelar-Nicolau: Typology of Motivation Factors for Employees in the Banking Sector: An Empirical Study Using Multivariate Data Analysis Methods -- J. Michael Spoor, J. Weber, and J. Ovtcharova: A Proposal for Formalization and Definition of Anomalies in Dynamical Systems -- N. Tahiri and A. Koshkarov: New Metrics for Classifying Phylogenetic Trees Using -means and the Symmetric Difference Metric -- S. D. Tomarchio: On Parsimonious Modelling via Matrix-variate t Mixtures -- G. Zammarchi, M. Romano, and C. Conversano: Evolution of Media Coverage on Climate Change and Environmental Awareness: an Analysis of Tweets from UK and US Newspapers.
506 $a Open access.
520 $a 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.
650 2 4 $a Statistical Software. $3 1390759
650 2 4 $a Multivariate Analysis. $3 563891
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Statistical Learning. $3 1396099
650 1 4 $a Data Science. $3 1174436
650 0 $a Classification $v Congresses. $3 882996
650 0 $a Big data $v Congresses. $3 882536
700 1 $a Brito, Paula. $3 882169
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Studies in classification, data analysis, and knowledge organization. $3 639903
856 4 0 $u https://doi.org/10.1007/978-3-031-09034-9
950 $a Mathematics and Statistics (SpringerNature-11649)