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Biblioteca

MARC Lista (tabellare)
Advances in Probabilistic Databases for Uncertain Information Management / / edited by Zongmin Ma, Li Yan
Advances in Probabilistic Databases for Uncertain Information Management / / edited by Zongmin Ma, Li Yan
Edizione [1st ed. 2013.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013
Descrizione fisica 1 online resource (X, 161 p. 23 illus.)
Disciplina 005.74068
Collana Studies in Fuzziness and Soft Computing
Soggetto topico Applied mathematics
Engineering mathematics
Artificial intelligence
Application software
Mathematical and Computational Engineering
Artificial Intelligence
Information Systems Applications (incl. Internet)
ISBN 9783642375095
364237509X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1   Research in Probabilistic Spatiotemporal Databases: The SPOT Framework -- 2   A Probabilistic Object-Oriented Database Model with Fuzzy Measures -- 3   Probabilistic XML: Models and Complexity -- 4   Uncertain Data: Representations, Query Processing, and Applications -- 5   A Theoretically-Sound Approach for OLAPing Uncertain and Imprecise Multidimensional Data Streams -- 6   Tractable Probabilistic Description Logic Programs.  .
Record Nr. UNINA-9910437891703321
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Applied data mining for business and industry [[electronic resource] /] / Paolo Giudici, Silvia Figini
Applied data mining for business and industry [[electronic resource] /] / Paolo Giudici, Silvia Figini
Autore Giudici Paolo
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, NJ, : John Wiley, 2009
Descrizione fisica 1 online resource (259 p.)
Disciplina 005.74068
006.312
Altri autori (Persone) FiginiSilvia
Soggetto topico Data mining
Business - Data processing
Commercial statistics
Soggetto genere / forma Electronic books.
ISBN 1-282-12369-6
9786612123696
0-470-05886-2
0-470-74583-5
0-470-74582-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied Data Mining for Business and Industry; Contents; 1 Introduction; Part I Methodology; 2 Organisation of the data; 2.1 Statistical units and statistical variables; 2.2 Data matrices and their transformations; 2.3 Complex data structures; 2.4 Summary; 3 Summary statistics; 3.1 Univariate exploratory analysis; 3.1.1 Measures of location; 3.1.2 Measures of variability; 3.1.3 Measures of heterogeneity; 3.1.4 Measures of concentration; 3.1.5 Measures of asymmetry; 3.1.6 Measures of kurtosis; 3.2 Bivariate exploratory analysis of quantitative data
3.3 Multivariate exploratory analysis of quantitative data3.4 Multivariate exploratory analysis of qualitative data; 3.4.1 Independence and association; 3.4.2 Distance measures; 3.4.3 Dependency measures; 3.4.4 Model-based measures; 3.5 Reduction of dimensionality; 3.5.1 Interpretation of the principal components; 3.6 Further reading; 4 Model specification; 4.1 Measures of distance; 4.1.1 Euclidean distance; 4.1.2 Similarity measures; 4.1.3 Multidimensional scaling; 4.2 Cluster analysis; 4.2.1 Hierarchical methods; 4.2.2 Evaluation of hierarchical methods; 4.2.3 Non-hierarchical methods
4.3 Linear regression4.3.1 Bivariate linear regression; 4.3.2 Properties of the residuals; 4.3.3 Goodness of fit; 4.3.4 Multiple linear regression; 4.4 Logistic regression; 4.4.1 Interpretation of logistic regression; 4.4.2 Discriminant analysis; 4.5 Tree models; 4.5.1 Division criteria; 4.5.2 Pruning; 4.6 Neural networks; 4.6.1 Architecture of a neural network; 4.6.2 The multilayer perceptron; 4.6.3 Kohonen networks; 4.7 Nearest-neighbour models; 4.8 Local models; 4.8.1 Association rules; 4.8.2 Retrieval by content; 4.9 Uncertainty measures and inference; 4.9.1 Probability
4.9.2 Statistical models4.9.3 Statistical inference; 4.10 Non-parametric modelling; 4.11 The normal linear model; 4.11.1 Main inferential results; 4.12 Generalised linear models; 4.12.1 The exponential family; 4.12.2 Definition of generalised linear models; 4.12.3 The logistic regression model; 4.13 Log-linear models; 4.13.1 Construction of a log-linear model; 4.13.2 Interpretation of a log-linear model; 4.13.3 Graphical log-linear models; 4.13.4 Log-linear model comparison; 4.14 Graphical models; 4.14.1 Symmetric graphical models; 4.14.2 Recursive graphical models
4.14.3 Graphical models and neural networks4.15 Survival analysis models; 4.16 Further reading; 5 Model evaluation; 5.1 Criteria based on statistical tests; 5.1.1 Distance between statistical models; 5.1.2 Discrepancy of a statistical model; 5.1.3 Kullback-Leibler discrepancy; 5.2 Criteria based on scoring functions; 5.3 Bayesian criteria; 5.4 Computational criteria; 5.5 Criteria based on loss functions; 5.6 Further reading; Part II Business case studies; 6 Describing website visitors; 6.1 Objectives of the analysis; 6.2 Description of the data; 6.3 Exploratory analysis; 6.4 Model building
6.4.1 Cluster analysis
Record Nr. UNINA-9910146147203321
Giudici Paolo  
Hoboken, NJ, : John Wiley, 2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied data mining for business and industry [[electronic resource] /] / Paolo Giudici, Silvia Figini
Applied data mining for business and industry [[electronic resource] /] / Paolo Giudici, Silvia Figini
Autore Giudici Paolo
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, NJ, : John Wiley, 2009
Descrizione fisica 1 online resource (259 p.)
Disciplina 005.74068
006.312
Altri autori (Persone) FiginiSilvia
Soggetto topico Data mining
Business - Data processing
Commercial statistics
ISBN 1-282-12369-6
9786612123696
0-470-05886-2
0-470-74583-5
0-470-74582-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied Data Mining for Business and Industry; Contents; 1 Introduction; Part I Methodology; 2 Organisation of the data; 2.1 Statistical units and statistical variables; 2.2 Data matrices and their transformations; 2.3 Complex data structures; 2.4 Summary; 3 Summary statistics; 3.1 Univariate exploratory analysis; 3.1.1 Measures of location; 3.1.2 Measures of variability; 3.1.3 Measures of heterogeneity; 3.1.4 Measures of concentration; 3.1.5 Measures of asymmetry; 3.1.6 Measures of kurtosis; 3.2 Bivariate exploratory analysis of quantitative data
3.3 Multivariate exploratory analysis of quantitative data3.4 Multivariate exploratory analysis of qualitative data; 3.4.1 Independence and association; 3.4.2 Distance measures; 3.4.3 Dependency measures; 3.4.4 Model-based measures; 3.5 Reduction of dimensionality; 3.5.1 Interpretation of the principal components; 3.6 Further reading; 4 Model specification; 4.1 Measures of distance; 4.1.1 Euclidean distance; 4.1.2 Similarity measures; 4.1.3 Multidimensional scaling; 4.2 Cluster analysis; 4.2.1 Hierarchical methods; 4.2.2 Evaluation of hierarchical methods; 4.2.3 Non-hierarchical methods
4.3 Linear regression4.3.1 Bivariate linear regression; 4.3.2 Properties of the residuals; 4.3.3 Goodness of fit; 4.3.4 Multiple linear regression; 4.4 Logistic regression; 4.4.1 Interpretation of logistic regression; 4.4.2 Discriminant analysis; 4.5 Tree models; 4.5.1 Division criteria; 4.5.2 Pruning; 4.6 Neural networks; 4.6.1 Architecture of a neural network; 4.6.2 The multilayer perceptron; 4.6.3 Kohonen networks; 4.7 Nearest-neighbour models; 4.8 Local models; 4.8.1 Association rules; 4.8.2 Retrieval by content; 4.9 Uncertainty measures and inference; 4.9.1 Probability
4.9.2 Statistical models4.9.3 Statistical inference; 4.10 Non-parametric modelling; 4.11 The normal linear model; 4.11.1 Main inferential results; 4.12 Generalised linear models; 4.12.1 The exponential family; 4.12.2 Definition of generalised linear models; 4.12.3 The logistic regression model; 4.13 Log-linear models; 4.13.1 Construction of a log-linear model; 4.13.2 Interpretation of a log-linear model; 4.13.3 Graphical log-linear models; 4.13.4 Log-linear model comparison; 4.14 Graphical models; 4.14.1 Symmetric graphical models; 4.14.2 Recursive graphical models
4.14.3 Graphical models and neural networks4.15 Survival analysis models; 4.16 Further reading; 5 Model evaluation; 5.1 Criteria based on statistical tests; 5.1.1 Distance between statistical models; 5.1.2 Discrepancy of a statistical model; 5.1.3 Kullback-Leibler discrepancy; 5.2 Criteria based on scoring functions; 5.3 Bayesian criteria; 5.4 Computational criteria; 5.5 Criteria based on loss functions; 5.6 Further reading; Part II Business case studies; 6 Describing website visitors; 6.1 Objectives of the analysis; 6.2 Description of the data; 6.3 Exploratory analysis; 6.4 Model building
6.4.1 Cluster analysis
Record Nr. UNINA-9910830379703321
Giudici Paolo  
Hoboken, NJ, : John Wiley, 2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied data mining for business and industry / / Paolo Giudici, Silvia Figini
Applied data mining for business and industry / / Paolo Giudici, Silvia Figini
Autore Giudici Paolo
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, NJ, : John Wiley, 2009
Descrizione fisica 1 online resource (259 p.)
Disciplina 005.74068
006.312
Altri autori (Persone) FiginiSilvia
Soggetto topico Data mining
Business - Data processing
Commercial statistics
ISBN 9786612123696
9781282123694
1282123696
9780470058862
0470058862
9780470745830
0470745835
9780470745823
0470745827
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied Data Mining for Business and Industry; Contents; 1 Introduction; Part I Methodology; 2 Organisation of the data; 2.1 Statistical units and statistical variables; 2.2 Data matrices and their transformations; 2.3 Complex data structures; 2.4 Summary; 3 Summary statistics; 3.1 Univariate exploratory analysis; 3.1.1 Measures of location; 3.1.2 Measures of variability; 3.1.3 Measures of heterogeneity; 3.1.4 Measures of concentration; 3.1.5 Measures of asymmetry; 3.1.6 Measures of kurtosis; 3.2 Bivariate exploratory analysis of quantitative data
3.3 Multivariate exploratory analysis of quantitative data3.4 Multivariate exploratory analysis of qualitative data; 3.4.1 Independence and association; 3.4.2 Distance measures; 3.4.3 Dependency measures; 3.4.4 Model-based measures; 3.5 Reduction of dimensionality; 3.5.1 Interpretation of the principal components; 3.6 Further reading; 4 Model specification; 4.1 Measures of distance; 4.1.1 Euclidean distance; 4.1.2 Similarity measures; 4.1.3 Multidimensional scaling; 4.2 Cluster analysis; 4.2.1 Hierarchical methods; 4.2.2 Evaluation of hierarchical methods; 4.2.3 Non-hierarchical methods
4.3 Linear regression4.3.1 Bivariate linear regression; 4.3.2 Properties of the residuals; 4.3.3 Goodness of fit; 4.3.4 Multiple linear regression; 4.4 Logistic regression; 4.4.1 Interpretation of logistic regression; 4.4.2 Discriminant analysis; 4.5 Tree models; 4.5.1 Division criteria; 4.5.2 Pruning; 4.6 Neural networks; 4.6.1 Architecture of a neural network; 4.6.2 The multilayer perceptron; 4.6.3 Kohonen networks; 4.7 Nearest-neighbour models; 4.8 Local models; 4.8.1 Association rules; 4.8.2 Retrieval by content; 4.9 Uncertainty measures and inference; 4.9.1 Probability
4.9.2 Statistical models4.9.3 Statistical inference; 4.10 Non-parametric modelling; 4.11 The normal linear model; 4.11.1 Main inferential results; 4.12 Generalised linear models; 4.12.1 The exponential family; 4.12.2 Definition of generalised linear models; 4.12.3 The logistic regression model; 4.13 Log-linear models; 4.13.1 Construction of a log-linear model; 4.13.2 Interpretation of a log-linear model; 4.13.3 Graphical log-linear models; 4.13.4 Log-linear model comparison; 4.14 Graphical models; 4.14.1 Symmetric graphical models; 4.14.2 Recursive graphical models
4.14.3 Graphical models and neural networks4.15 Survival analysis models; 4.16 Further reading; 5 Model evaluation; 5.1 Criteria based on statistical tests; 5.1.1 Distance between statistical models; 5.1.2 Discrepancy of a statistical model; 5.1.3 Kullback-Leibler discrepancy; 5.2 Criteria based on scoring functions; 5.3 Bayesian criteria; 5.4 Computational criteria; 5.5 Criteria based on loss functions; 5.6 Further reading; Part II Business case studies; 6 Describing website visitors; 6.1 Objectives of the analysis; 6.2 Description of the data; 6.3 Exploratory analysis; 6.4 Model building
6.4.1 Cluster analysis
Record Nr. UNINA-9911019548903321
Giudici Paolo  
Hoboken, NJ, : John Wiley, 2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Big data e innovazione computazionale / Andrea Ottolia
Big data e innovazione computazionale / Andrea Ottolia
Autore Ottolia, Andrea <1976- >
Pubbl/distr/stampa Torino, : G. Giappichelli Editore, [2017]
Descrizione fisica XII, 363 p. ; 24 cm
Disciplina 005.74068
346.048028563
Collana Quaderni di AIDA
Soggetto non controllato Big data - Tutela [della] proprietà immateriale
ISBN 978-88-921-1234-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNINA-9910269060203321
Ottolia, Andrea <1976- >  
Torino, : G. Giappichelli Editore, [2017]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Data mining : metodi informatici, statistici e applicazioni / Paolo Giudici
Data mining : metodi informatici, statistici e applicazioni / Paolo Giudici
Autore Giudici, Paolo
Edizione [2. ed.]
Pubbl/distr/stampa Milano : McGraw-Hill, 2005
Descrizione fisica xii, 401 p. ; 24 cm
Disciplina 005.74068
Collana Workbooks
Soggetto topico Aziende - Gestione - Metodi statistici
Aziende - Sistemi informativi - Automazione
Aziende - Archivi di dati - Gestione
ISBN 8838662312
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNISALENTO-991001592169707536
Giudici, Paolo  
Milano : McGraw-Hill, 2005
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Data mining : metodi statistici per le applicazioni aziendali / Paolo Giudici
Data mining : metodi statistici per le applicazioni aziendali / Paolo Giudici
Autore Giudici, Paolo
Pubbl/distr/stampa Milano [etc.] : McGraw-Hill, [2001]
Descrizione fisica XIII, 424 p. ; 24 cm
Disciplina 005.74068
Collana Workbooks
Soggetto topico Aziende - Gestione - Metodi statistici
ISBN 8838660018
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNISALENTO-991000382379707536
Giudici, Paolo  
Milano [etc.] : McGraw-Hill, [2001]
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Intelligent Data Engineering and Automated Learning - IDEAL 2007 : 8th International Conference, Birmingham, UK, December 16-19, 2007, Proceedings / / edited by Hujun Yin, Xin Yao, Peter Tino, Emilio Corchado, Will Byrne
Intelligent Data Engineering and Automated Learning - IDEAL 2007 : 8th International Conference, Birmingham, UK, December 16-19, 2007, Proceedings / / edited by Hujun Yin, Xin Yao, Peter Tino, Emilio Corchado, Will Byrne
Edizione [1st ed. 2007.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2007
Descrizione fisica 1 online resource (XX, 1174 p.)
Disciplina 005.74068
Collana Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico Data structures (Computer science)
Information theory
Database management
Algorithms
Artificial intelligence
Application software
Data mining
Data Structures and Information Theory
Database Management
Artificial Intelligence
Computer and Information Systems Applications
Data Mining and Knowledge Discovery
ISBN 3-540-77226-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Learning and Information Processing -- Data Mining and Information Management -- Bioinformatics and Neuroinformatics -- Agents and Distributed Systems -- Financial Engineering and Modelling -- Agent-Based Approach to Service Sciences -- Neural-evolutionary Fusion Algorithms and Their Applications.
Record Nr. UNINA-9910484428703321
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Intelligent data engineering and automated learning -- IDEAL 2009 : 10th international conference, Burgos, Spain, September 23-26, 2009 : proceedings / / edited by Hujun Yin [and three others]
Intelligent data engineering and automated learning -- IDEAL 2009 : 10th international conference, Burgos, Spain, September 23-26, 2009 : proceedings / / edited by Hujun Yin [and three others]
Edizione [1st ed. 2007.]
Pubbl/distr/stampa Berlin, Germany ; ; New York, New York : , : Springer, , [2007]
Descrizione fisica 1 online resource (XX, 1174 p.)
Disciplina 005.74068
Collana Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico Intelligent agents (Computer software)
Database management
ISBN 3-540-77226-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Learning and Information Processing -- Data Mining and Information Management -- Bioinformatics and Neuroinformatics -- Agents and Distributed Systems -- Financial Engineering and Modelling -- Agent-Based Approach to Service Sciences -- Neural-evolutionary Fusion Algorithms and Their Applications.
Record Nr. UNISA-996466244003316
Berlin, Germany ; ; New York, New York : , : Springer, , [2007]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
International Conference on Scientific and Statistical Database Management : [proceedings] / / SSDBM
International Conference on Scientific and Statistical Database Management : [proceedings] / / SSDBM
Pubbl/distr/stampa Los Alamitos, Calif., : IEEE Computer Society, c2003-
Descrizione fisica v. : ill. ; ; 28 cm
Disciplina 005
005.74068
Soggetto topico Database design
Database management
Mathematical statistics - Data processing
Science - Data processing
Soggetto genere / forma Conference papers and proceedings.
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Record Nr. UNINA-9910145483803321
Los Alamitos, Calif., : IEEE Computer Society, c2003-
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui