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Biblioteca

MARC Lista (tabellare)
Agent-based computational economics using Netlogo [[electronic resource] /] / authored by Romulus-Catalin Damaceanu
Agent-based computational economics using Netlogo [[electronic resource] /] / authored by Romulus-Catalin Damaceanu
Autore Damaceanu Romulus-Catalin
Pubbl/distr/stampa [Sharjah, U.A.E.], : Bentham Science Publishers, 2013
Descrizione fisica 1 online resource (157 p.)
Disciplina 330.015195
Soggetto topico Electronic data processing - Data preparation
Computer networks
ISBN 1-60805-489-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 01 Cover.pdf; 02 TItle; 03 REVISED Online; 04 Contents; 05 Foreword; 06 Preface; 07 Chapter 01; 08 Chapter 02; 09 Chapter 03; 10 Chapter 04; 11 Chapter 05; 12 Appendix; 13 Index
Record Nr. UNINA-9910779624803321
Damaceanu Romulus-Catalin  
[Sharjah, U.A.E.], : Bentham Science Publishers, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Agent-based computational economics using Netlogo [[electronic resource] /] / authored by Romulus-Catalin Damaceanu
Agent-based computational economics using Netlogo [[electronic resource] /] / authored by Romulus-Catalin Damaceanu
Pubbl/distr/stampa [Sharjah, U.A.E.], : Bentham Science Publishers, 2013
Descrizione fisica 1 online resource (157 p.)
Disciplina 330.015195
Soggetto topico Electronic data processing - Data preparation
Computer networks
Soggetto genere / forma Electronic books.
ISBN 1-60805-489-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 01 Cover.pdf; 02 TItle; 03 REVISED Online; 04 Contents; 05 Foreword; 06 Preface; 07 Chapter 01; 08 Chapter 02; 09 Chapter 03; 10 Chapter 04; 11 Chapter 05; 12 Appendix; 13 Index
Record Nr. UNINA-9910453049503321
[Sharjah, U.A.E.], : Bentham Science Publishers, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Creating good data : a guide to dataset structure and data representation / / Harry J. Foxwell
Creating good data : a guide to dataset structure and data representation / / Harry J. Foxwell
Autore Foxwell Harry J.
Edizione [1st ed. 2020.]
Pubbl/distr/stampa [Place of publication not identified] : , : Apress, , [2020]
Descrizione fisica 1 online resource (112 pages)
Disciplina 005.72
Soggetto topico Electronic data processing - Data preparation
ISBN 1-4842-6103-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: The Need for Good Data -- Chapter 2: Basic Data Types and When to Use Them -- Chapter 3: Representing Quantitative Data -- Chapter 4: Planning Your Data Collection and Analysis -- Chapter 5: Good Datasets -- Chapter 6: Good Data Collection -- Chapter 7: Dataset Examples and Use Cases -- Chapter 8: Cleaning your Data -- Chapter 9: Good Data Anayltics -- Appendix A: Recommended Reading.
Record Nr. UNINA-9910427042203321
Foxwell Harry J.  
[Place of publication not identified] : , : Apress, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Exploratory data mining and data cleaning [[electronic resource] /] / Tamraparni Dasu, Theorodre Johnson
Exploratory data mining and data cleaning [[electronic resource] /] / Tamraparni Dasu, Theorodre Johnson
Autore Dasu Tamraparni
Pubbl/distr/stampa New York, : Wiley-Interscience, 2003
Descrizione fisica 1 online resource (226 p.)
Disciplina 005.741
006.3
006.312
Altri autori (Persone) JohnsonTheodore
Collana Wiley series in probability and statistics
Soggetto topico Data mining
Electronic data processing - Data preparation
Electronic data processing - Quality control
Soggetto genere / forma Electronic books.
ISBN 1-280-36625-7
9786610366255
0-470-30781-1
0-471-45864-3
0-471-44835-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Exploratory Data Mining and Data Cleaning; Contents; Preface; 1. Exploratory Data Mining and Data Cleaning: An Overview; 1.1 Introduction; 1.2 Cautionary Tales; 1.3 Taming the Data; 1.4 Challenges; 1.5 Methods; 1.6 EDM; 1.6.1 EDM Summaries-Parametric; 1.6.2 EDM Summaries-Nonparametric; 1.7 End-to-End Data Quality (DQ); 1.7.1 DQ in Data Preparation; 1.7.2 EDM and Data Glitches; 1.7.3 Tools for DQ; 1.7.4 End-to-End DQ: The Data Quality Continuum; 1.7.5 Measuring Data Quality; 1.8 Conclusion; 2. Exploratory Data Mining; 2.1 Introduction; 2.2 Uncertainty; 2.2.1 Annotated Bibliography
2.3 EDM: Exploratory Data Mining2.4 EDM Summaries; 2.4.1 Typical Values; 2.4.2 Attribute Variation; 2.4.3 Example; 2.4.4 Attribute Relationships; 2.4.5 Annotated Bibliography; 2.5 What Makes a Summary Useful?; 2.5.1 Statistical Properties; 2.5.2 Computational Criteria; 2.5.3 Annotated Bibliography; 2.6 Data-Driven Approach-Nonparametric Analysis; 2.6.1 The Joy of Counting; 2.6.2 Empirical Cumulative Distribution Function (ECDF); 2.6.3 Univariate Histograms; 2.6.4 Annotated Bibliography; 2.7 EDM in Higher Dimensions; 2.8 Rectilinear Histograms; 2.9 Depth and Multivariate Binning
2.9.1 Data Depth2.9.2 Aside: Depth-Related Topics; 2.9.3 Annotated Bibliography; 2.10 Conclusion; 3. Partitions and Piecewise Models; 3.1 Divide and Conquer; 3.1.1 Why Do We Need Partitions?; 3.1.2 Dividing Data; 3.1.3 Applications of Partition-Based EDM Summaries; 3.2 Axis-Aligned Partitions and Data Cubes; 3.2.1 Annotated Bibliography; 3.3 Nonlinear Partitions; 3.3.1 Annotated Bibliography; 3.4 DataSpheres (DS); 3.4.1 Layers; 3.4.2 Data Pyramids; 3.4.3 EDM Summaries; 3.4.4 Annotated Bibliography; 3.5 Set Comparison Using EDM Summaries; 3.5.1 Motivation; 3.5.2 Comparison Strategy
3.5.3 Statistical Tests for Change3.5.4 Application-Two Case Studies; 3.5.5 Annotated Bibliography; 3.6 Discovering Complex Structure in Data with EDM Summaries; 3.6.1 Exploratory Model Fitting in Interactive Response Time; 3.6.2 Annotated Bibliography; 3.7 Piecewise Linear Regression; 3.7.1 An Application; 3.7.2 Regression Coefficients; 3.7.3 Improvement in Fit; 3.7.4 Annotated Bibliography; 3.8 One-Pass Classification; 3.8.1 Quantile-Based Prediction with Piecewise Models; 3.8.2 Simulation Study; 3.8.3 Annotated Bibliography; 3.9 Conclusion; 4. Data Quality; 4.1 Introduction
4.2 The Meaning of Data Quality4.2.1 An Example; 4.2.2 Data Glitches; 4.2.3 Conventional Definition of DQ; 4.2.4 Times Have Changed; 4.2.5 Annotated Bibliography; 4.3 Updating DQ Metrics: Data Quality Continuum; 4.3.1 Data Gathering; 4.3.2 Data Delivery; 4.3.3 Data Monitoring; 4.3.4 Data Storage; 4.3.5 Data Integration; 4.3.6 Data Retrieval; 4.3.7 Data Mining/Analysis; 4.3.8 Annotated Bibliography; 4.4 The Meaning of Data Quality Revisited; 4.4.1 Data Interpretation; 4.4.2 Data Suitability; 4.4.3 Dataset Type; 4.4.4 Attribute Type; 4.4.5 Application Type
4.4.6 Data Quality-A Many Splendored Thing
Record Nr. UNINA-9910146077903321
Dasu Tamraparni  
New York, : Wiley-Interscience, 2003
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Exploratory data mining and data cleaning [[electronic resource] /] / Tamraparni Dasu, Theorodre Johnson
Exploratory data mining and data cleaning [[electronic resource] /] / Tamraparni Dasu, Theorodre Johnson
Autore Dasu Tamraparni
Pubbl/distr/stampa New York, : Wiley-Interscience, 2003
Descrizione fisica 1 online resource (226 p.)
Disciplina 005.741
006.3
006.312
Altri autori (Persone) JohnsonTheodore
Collana Wiley series in probability and statistics
Soggetto topico Data mining
Electronic data processing - Data preparation
Electronic data processing - Quality control
ISBN 1-280-36625-7
9786610366255
0-470-30781-1
0-471-45864-3
0-471-44835-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Exploratory Data Mining and Data Cleaning; Contents; Preface; 1. Exploratory Data Mining and Data Cleaning: An Overview; 1.1 Introduction; 1.2 Cautionary Tales; 1.3 Taming the Data; 1.4 Challenges; 1.5 Methods; 1.6 EDM; 1.6.1 EDM Summaries-Parametric; 1.6.2 EDM Summaries-Nonparametric; 1.7 End-to-End Data Quality (DQ); 1.7.1 DQ in Data Preparation; 1.7.2 EDM and Data Glitches; 1.7.3 Tools for DQ; 1.7.4 End-to-End DQ: The Data Quality Continuum; 1.7.5 Measuring Data Quality; 1.8 Conclusion; 2. Exploratory Data Mining; 2.1 Introduction; 2.2 Uncertainty; 2.2.1 Annotated Bibliography
2.3 EDM: Exploratory Data Mining2.4 EDM Summaries; 2.4.1 Typical Values; 2.4.2 Attribute Variation; 2.4.3 Example; 2.4.4 Attribute Relationships; 2.4.5 Annotated Bibliography; 2.5 What Makes a Summary Useful?; 2.5.1 Statistical Properties; 2.5.2 Computational Criteria; 2.5.3 Annotated Bibliography; 2.6 Data-Driven Approach-Nonparametric Analysis; 2.6.1 The Joy of Counting; 2.6.2 Empirical Cumulative Distribution Function (ECDF); 2.6.3 Univariate Histograms; 2.6.4 Annotated Bibliography; 2.7 EDM in Higher Dimensions; 2.8 Rectilinear Histograms; 2.9 Depth and Multivariate Binning
2.9.1 Data Depth2.9.2 Aside: Depth-Related Topics; 2.9.3 Annotated Bibliography; 2.10 Conclusion; 3. Partitions and Piecewise Models; 3.1 Divide and Conquer; 3.1.1 Why Do We Need Partitions?; 3.1.2 Dividing Data; 3.1.3 Applications of Partition-Based EDM Summaries; 3.2 Axis-Aligned Partitions and Data Cubes; 3.2.1 Annotated Bibliography; 3.3 Nonlinear Partitions; 3.3.1 Annotated Bibliography; 3.4 DataSpheres (DS); 3.4.1 Layers; 3.4.2 Data Pyramids; 3.4.3 EDM Summaries; 3.4.4 Annotated Bibliography; 3.5 Set Comparison Using EDM Summaries; 3.5.1 Motivation; 3.5.2 Comparison Strategy
3.5.3 Statistical Tests for Change3.5.4 Application-Two Case Studies; 3.5.5 Annotated Bibliography; 3.6 Discovering Complex Structure in Data with EDM Summaries; 3.6.1 Exploratory Model Fitting in Interactive Response Time; 3.6.2 Annotated Bibliography; 3.7 Piecewise Linear Regression; 3.7.1 An Application; 3.7.2 Regression Coefficients; 3.7.3 Improvement in Fit; 3.7.4 Annotated Bibliography; 3.8 One-Pass Classification; 3.8.1 Quantile-Based Prediction with Piecewise Models; 3.8.2 Simulation Study; 3.8.3 Annotated Bibliography; 3.9 Conclusion; 4. Data Quality; 4.1 Introduction
4.2 The Meaning of Data Quality4.2.1 An Example; 4.2.2 Data Glitches; 4.2.3 Conventional Definition of DQ; 4.2.4 Times Have Changed; 4.2.5 Annotated Bibliography; 4.3 Updating DQ Metrics: Data Quality Continuum; 4.3.1 Data Gathering; 4.3.2 Data Delivery; 4.3.3 Data Monitoring; 4.3.4 Data Storage; 4.3.5 Data Integration; 4.3.6 Data Retrieval; 4.3.7 Data Mining/Analysis; 4.3.8 Annotated Bibliography; 4.4 The Meaning of Data Quality Revisited; 4.4.1 Data Interpretation; 4.4.2 Data Suitability; 4.4.3 Dataset Type; 4.4.4 Attribute Type; 4.4.5 Application Type
4.4.6 Data Quality-A Many Splendored Thing
Record Nr. UNISA-996211655103316
Dasu Tamraparni  
New York, : Wiley-Interscience, 2003
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Exploratory data mining and data cleaning [[electronic resource] /] / Tamraparni Dasu, Theorodre Johnson
Exploratory data mining and data cleaning [[electronic resource] /] / Tamraparni Dasu, Theorodre Johnson
Autore Dasu Tamraparni
Pubbl/distr/stampa New York, : Wiley-Interscience, 2003
Descrizione fisica 1 online resource (226 p.)
Disciplina 005.741
006.3
006.312
Altri autori (Persone) JohnsonTheodore
Collana Wiley series in probability and statistics
Soggetto topico Data mining
Electronic data processing - Data preparation
Electronic data processing - Quality control
ISBN 1-280-36625-7
9786610366255
0-470-30781-1
0-471-45864-3
0-471-44835-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Exploratory Data Mining and Data Cleaning; Contents; Preface; 1. Exploratory Data Mining and Data Cleaning: An Overview; 1.1 Introduction; 1.2 Cautionary Tales; 1.3 Taming the Data; 1.4 Challenges; 1.5 Methods; 1.6 EDM; 1.6.1 EDM Summaries-Parametric; 1.6.2 EDM Summaries-Nonparametric; 1.7 End-to-End Data Quality (DQ); 1.7.1 DQ in Data Preparation; 1.7.2 EDM and Data Glitches; 1.7.3 Tools for DQ; 1.7.4 End-to-End DQ: The Data Quality Continuum; 1.7.5 Measuring Data Quality; 1.8 Conclusion; 2. Exploratory Data Mining; 2.1 Introduction; 2.2 Uncertainty; 2.2.1 Annotated Bibliography
2.3 EDM: Exploratory Data Mining2.4 EDM Summaries; 2.4.1 Typical Values; 2.4.2 Attribute Variation; 2.4.3 Example; 2.4.4 Attribute Relationships; 2.4.5 Annotated Bibliography; 2.5 What Makes a Summary Useful?; 2.5.1 Statistical Properties; 2.5.2 Computational Criteria; 2.5.3 Annotated Bibliography; 2.6 Data-Driven Approach-Nonparametric Analysis; 2.6.1 The Joy of Counting; 2.6.2 Empirical Cumulative Distribution Function (ECDF); 2.6.3 Univariate Histograms; 2.6.4 Annotated Bibliography; 2.7 EDM in Higher Dimensions; 2.8 Rectilinear Histograms; 2.9 Depth and Multivariate Binning
2.9.1 Data Depth2.9.2 Aside: Depth-Related Topics; 2.9.3 Annotated Bibliography; 2.10 Conclusion; 3. Partitions and Piecewise Models; 3.1 Divide and Conquer; 3.1.1 Why Do We Need Partitions?; 3.1.2 Dividing Data; 3.1.3 Applications of Partition-Based EDM Summaries; 3.2 Axis-Aligned Partitions and Data Cubes; 3.2.1 Annotated Bibliography; 3.3 Nonlinear Partitions; 3.3.1 Annotated Bibliography; 3.4 DataSpheres (DS); 3.4.1 Layers; 3.4.2 Data Pyramids; 3.4.3 EDM Summaries; 3.4.4 Annotated Bibliography; 3.5 Set Comparison Using EDM Summaries; 3.5.1 Motivation; 3.5.2 Comparison Strategy
3.5.3 Statistical Tests for Change3.5.4 Application-Two Case Studies; 3.5.5 Annotated Bibliography; 3.6 Discovering Complex Structure in Data with EDM Summaries; 3.6.1 Exploratory Model Fitting in Interactive Response Time; 3.6.2 Annotated Bibliography; 3.7 Piecewise Linear Regression; 3.7.1 An Application; 3.7.2 Regression Coefficients; 3.7.3 Improvement in Fit; 3.7.4 Annotated Bibliography; 3.8 One-Pass Classification; 3.8.1 Quantile-Based Prediction with Piecewise Models; 3.8.2 Simulation Study; 3.8.3 Annotated Bibliography; 3.9 Conclusion; 4. Data Quality; 4.1 Introduction
4.2 The Meaning of Data Quality4.2.1 An Example; 4.2.2 Data Glitches; 4.2.3 Conventional Definition of DQ; 4.2.4 Times Have Changed; 4.2.5 Annotated Bibliography; 4.3 Updating DQ Metrics: Data Quality Continuum; 4.3.1 Data Gathering; 4.3.2 Data Delivery; 4.3.3 Data Monitoring; 4.3.4 Data Storage; 4.3.5 Data Integration; 4.3.6 Data Retrieval; 4.3.7 Data Mining/Analysis; 4.3.8 Annotated Bibliography; 4.4 The Meaning of Data Quality Revisited; 4.4.1 Data Interpretation; 4.4.2 Data Suitability; 4.4.3 Dataset Type; 4.4.4 Attribute Type; 4.4.5 Application Type
4.4.6 Data Quality-A Many Splendored Thing
Record Nr. UNINA-9910829874403321
Dasu Tamraparni  
New York, : Wiley-Interscience, 2003
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Exploratory data mining and data cleaning / / Tamraparni Dasu, Theorodre Johnson
Exploratory data mining and data cleaning / / Tamraparni Dasu, Theorodre Johnson
Autore Dasu Tamraparni
Pubbl/distr/stampa New York, : Wiley-Interscience, 2003
Descrizione fisica 1 online resource (226 p.)
Disciplina 006.3
Altri autori (Persone) JohnsonTheodore
Collana Wiley series in probability and statistics
Soggetto topico Data mining
Electronic data processing - Data preparation
Electronic data processing - Quality control
ISBN 1-280-36625-7
9786610366255
0-470-30781-1
0-471-45864-3
0-471-44835-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Exploratory Data Mining and Data Cleaning; Contents; Preface; 1. Exploratory Data Mining and Data Cleaning: An Overview; 1.1 Introduction; 1.2 Cautionary Tales; 1.3 Taming the Data; 1.4 Challenges; 1.5 Methods; 1.6 EDM; 1.6.1 EDM Summaries-Parametric; 1.6.2 EDM Summaries-Nonparametric; 1.7 End-to-End Data Quality (DQ); 1.7.1 DQ in Data Preparation; 1.7.2 EDM and Data Glitches; 1.7.3 Tools for DQ; 1.7.4 End-to-End DQ: The Data Quality Continuum; 1.7.5 Measuring Data Quality; 1.8 Conclusion; 2. Exploratory Data Mining; 2.1 Introduction; 2.2 Uncertainty; 2.2.1 Annotated Bibliography
2.3 EDM: Exploratory Data Mining2.4 EDM Summaries; 2.4.1 Typical Values; 2.4.2 Attribute Variation; 2.4.3 Example; 2.4.4 Attribute Relationships; 2.4.5 Annotated Bibliography; 2.5 What Makes a Summary Useful?; 2.5.1 Statistical Properties; 2.5.2 Computational Criteria; 2.5.3 Annotated Bibliography; 2.6 Data-Driven Approach-Nonparametric Analysis; 2.6.1 The Joy of Counting; 2.6.2 Empirical Cumulative Distribution Function (ECDF); 2.6.3 Univariate Histograms; 2.6.4 Annotated Bibliography; 2.7 EDM in Higher Dimensions; 2.8 Rectilinear Histograms; 2.9 Depth and Multivariate Binning
2.9.1 Data Depth2.9.2 Aside: Depth-Related Topics; 2.9.3 Annotated Bibliography; 2.10 Conclusion; 3. Partitions and Piecewise Models; 3.1 Divide and Conquer; 3.1.1 Why Do We Need Partitions?; 3.1.2 Dividing Data; 3.1.3 Applications of Partition-Based EDM Summaries; 3.2 Axis-Aligned Partitions and Data Cubes; 3.2.1 Annotated Bibliography; 3.3 Nonlinear Partitions; 3.3.1 Annotated Bibliography; 3.4 DataSpheres (DS); 3.4.1 Layers; 3.4.2 Data Pyramids; 3.4.3 EDM Summaries; 3.4.4 Annotated Bibliography; 3.5 Set Comparison Using EDM Summaries; 3.5.1 Motivation; 3.5.2 Comparison Strategy
3.5.3 Statistical Tests for Change3.5.4 Application-Two Case Studies; 3.5.5 Annotated Bibliography; 3.6 Discovering Complex Structure in Data with EDM Summaries; 3.6.1 Exploratory Model Fitting in Interactive Response Time; 3.6.2 Annotated Bibliography; 3.7 Piecewise Linear Regression; 3.7.1 An Application; 3.7.2 Regression Coefficients; 3.7.3 Improvement in Fit; 3.7.4 Annotated Bibliography; 3.8 One-Pass Classification; 3.8.1 Quantile-Based Prediction with Piecewise Models; 3.8.2 Simulation Study; 3.8.3 Annotated Bibliography; 3.9 Conclusion; 4. Data Quality; 4.1 Introduction
4.2 The Meaning of Data Quality4.2.1 An Example; 4.2.2 Data Glitches; 4.2.3 Conventional Definition of DQ; 4.2.4 Times Have Changed; 4.2.5 Annotated Bibliography; 4.3 Updating DQ Metrics: Data Quality Continuum; 4.3.1 Data Gathering; 4.3.2 Data Delivery; 4.3.3 Data Monitoring; 4.3.4 Data Storage; 4.3.5 Data Integration; 4.3.6 Data Retrieval; 4.3.7 Data Mining/Analysis; 4.3.8 Annotated Bibliography; 4.4 The Meaning of Data Quality Revisited; 4.4.1 Data Interpretation; 4.4.2 Data Suitability; 4.4.3 Dataset Type; 4.4.4 Attribute Type; 4.4.5 Application Type
4.4.6 Data Quality-A Many Splendored Thing
Record Nr. UNINA-9910876580303321
Dasu Tamraparni  
New York, : Wiley-Interscience, 2003
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui