top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Making sense of data [[electronic resource] ] : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt
Making sense of data [[electronic resource] ] : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt
Autore Myatt Glenn J. <1969->
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2007
Descrizione fisica 1 online resource (294 p.)
Disciplina 006.312
Soggetto topico Data mining
Mathematical statistics
ISBN 1-280-72178-2
9786610721788
0-470-10102-4
0-470-10101-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Making Sense of Data; Contents; Preface; 1. Introduction; 1.1 Overview; 1.2 Problem definition; 1.3 Data preparation; 1.4 Implementation of the analysis; 1.5 Deployment of the results; 1.6 Book outline; 1.7 Summary; 1.8 Further reading; 2. Definition; 2.1 Overview; 2.2 Objectives; 2.3 Deliverables; 2.4 Roles and responsibilities; 2.5 Project plan; 2.6 Case study; 2.6.1 Overview; 2.6.2 Problem; 2.6.3 Deliverables; 2.6.4 Roles and responsibilities; 2.6.5 Current situation; 2.6.6 Timetable and budget; 2.6.7 Cost/benefit analysis; 2.7 Summary; 2.8 Further reading; 3. Preparation; 3.1 Overview
3.2 Data sources3.3 Data understanding; 3.3.1 Data tables; 3.3.2 Continuous and discrete variables; 3.3.3 Scales of measurement; 3.3.4 Roles in analysis; 3.3.5 Frequency distribution; 3.4 Data preparation; 3.4.1 Overview; 3.4.2 Cleaning the data; 3.4.3 Removing variables; 3.4.4 Data transformations; 3.4.5 Segmentation; 3.5 Summary; 3.6 Exercises; 3.7 Further reading; 4. Tables and graphs; 4.1 Introduction; 4.2 Tables; 4.2.1 Data tables; 4.2.2 Contingency tables; 4.2.3 Summary tables; 4.3 Graphs; 4.3.1 Overview; 4.3.2 Frequency polygrams and histograms; 4.3.3 Scatterplots; 4.3.4 Box plots
4.3.5 Multiple graphs4.4 Summary; 4.5 Exercises; 4.6 Further reading; 5. Statistics; 5.1 Overview; 5.2 Descriptive statistics; 5.2.1 Overview; 5.2.2 Central tendency; 5.2.3 Variation; 5.2.4 Shape; 5.2.5 Example; 5.3 Inferential statistics; 5.3.1 Overview; 5.3.2 Confidence intervals; 5.3.3 Hypothesis tests; 5.3.4 Chi-square; 5.3.5 One-way analysis of variance; 5.4 Comparative statistics; 5.4.1 Overview; 5.4.2 Visualizing relationships; 5.4.3 Correlation coefficient (r); 5.4.4 Correlation analysis for more than two variables; 5.5 Summary; 5.6 Exercises; 5.7 Further reading; 6. Grouping
6.1 Introduction6.1.1 Overview; 6.1.2 Grouping by values or ranges; 6.1.3 Similarity measures; 6.1.4 Grouping approaches; 6.2 Clustering; 6.2.1 Overview; 6.2.2 Hierarchical agglomerative clustering; 6.2.3 K-means clustering; 6.3 Associative rules; 6.3.1 Overview; 6.3.2 Grouping by value combinations; 6.3.3 Extracting rules from groups; 6.3.4 Example; 6.4 Decision trees; 6.4.1 Overview; 6.4.2 Tree generation; 6.4.3 Splitting criteria; 6.4.4 Example; 6.5 Summary; 6.6 Exercises; 6.7 Further reading; 7. Prediction; 7.1 Introduction; 7.1.1 Overview; 7.1.2 Classification; 7.1.3 Regression
7.1.4 Building a prediction model7.1.5 Applying a prediction model; 7.2 Simple regression models; 7.2.1 Overview; 7.2.2 Simple linear regression; 7.2.3 Simple nonlinear regression; 7.3 K-nearest neighbors; 7.3.1 Overview; 7.3.2 Learning; 7.3.3 Prediction; 7.4 Classification and regression trees; 7.4.1 Overview; 7.4.2 Predicting using decision trees; 7.4.3 Example; 7.5 Neural networks; 7.5.1 Overview; 7.5.2 Neural network layers; 7.5.3 Node calculations; 7.5.4 Neural network predictions; 7.5.5 Learning process; 7.5.6 Backpropagation; 7.5.7 Using neural networks; 7.5.8 Example
7.6 Other methods
Record Nr. UNINA-9910143682503321
Myatt Glenn J. <1969->  
Hoboken, N.J., : Wiley-Interscience, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Making sense of data [[electronic resource] ] : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt
Making sense of data [[electronic resource] ] : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt
Autore Myatt Glenn J. <1969->
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2007
Descrizione fisica 1 online resource (294 p.)
Disciplina 006.312
Soggetto topico Data mining
Mathematical statistics
ISBN 1-280-72178-2
9786610721788
0-470-10102-4
0-470-10101-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Making Sense of Data; Contents; Preface; 1. Introduction; 1.1 Overview; 1.2 Problem definition; 1.3 Data preparation; 1.4 Implementation of the analysis; 1.5 Deployment of the results; 1.6 Book outline; 1.7 Summary; 1.8 Further reading; 2. Definition; 2.1 Overview; 2.2 Objectives; 2.3 Deliverables; 2.4 Roles and responsibilities; 2.5 Project plan; 2.6 Case study; 2.6.1 Overview; 2.6.2 Problem; 2.6.3 Deliverables; 2.6.4 Roles and responsibilities; 2.6.5 Current situation; 2.6.6 Timetable and budget; 2.6.7 Cost/benefit analysis; 2.7 Summary; 2.8 Further reading; 3. Preparation; 3.1 Overview
3.2 Data sources3.3 Data understanding; 3.3.1 Data tables; 3.3.2 Continuous and discrete variables; 3.3.3 Scales of measurement; 3.3.4 Roles in analysis; 3.3.5 Frequency distribution; 3.4 Data preparation; 3.4.1 Overview; 3.4.2 Cleaning the data; 3.4.3 Removing variables; 3.4.4 Data transformations; 3.4.5 Segmentation; 3.5 Summary; 3.6 Exercises; 3.7 Further reading; 4. Tables and graphs; 4.1 Introduction; 4.2 Tables; 4.2.1 Data tables; 4.2.2 Contingency tables; 4.2.3 Summary tables; 4.3 Graphs; 4.3.1 Overview; 4.3.2 Frequency polygrams and histograms; 4.3.3 Scatterplots; 4.3.4 Box plots
4.3.5 Multiple graphs4.4 Summary; 4.5 Exercises; 4.6 Further reading; 5. Statistics; 5.1 Overview; 5.2 Descriptive statistics; 5.2.1 Overview; 5.2.2 Central tendency; 5.2.3 Variation; 5.2.4 Shape; 5.2.5 Example; 5.3 Inferential statistics; 5.3.1 Overview; 5.3.2 Confidence intervals; 5.3.3 Hypothesis tests; 5.3.4 Chi-square; 5.3.5 One-way analysis of variance; 5.4 Comparative statistics; 5.4.1 Overview; 5.4.2 Visualizing relationships; 5.4.3 Correlation coefficient (r); 5.4.4 Correlation analysis for more than two variables; 5.5 Summary; 5.6 Exercises; 5.7 Further reading; 6. Grouping
6.1 Introduction6.1.1 Overview; 6.1.2 Grouping by values or ranges; 6.1.3 Similarity measures; 6.1.4 Grouping approaches; 6.2 Clustering; 6.2.1 Overview; 6.2.2 Hierarchical agglomerative clustering; 6.2.3 K-means clustering; 6.3 Associative rules; 6.3.1 Overview; 6.3.2 Grouping by value combinations; 6.3.3 Extracting rules from groups; 6.3.4 Example; 6.4 Decision trees; 6.4.1 Overview; 6.4.2 Tree generation; 6.4.3 Splitting criteria; 6.4.4 Example; 6.5 Summary; 6.6 Exercises; 6.7 Further reading; 7. Prediction; 7.1 Introduction; 7.1.1 Overview; 7.1.2 Classification; 7.1.3 Regression
7.1.4 Building a prediction model7.1.5 Applying a prediction model; 7.2 Simple regression models; 7.2.1 Overview; 7.2.2 Simple linear regression; 7.2.3 Simple nonlinear regression; 7.3 K-nearest neighbors; 7.3.1 Overview; 7.3.2 Learning; 7.3.3 Prediction; 7.4 Classification and regression trees; 7.4.1 Overview; 7.4.2 Predicting using decision trees; 7.4.3 Example; 7.5 Neural networks; 7.5.1 Overview; 7.5.2 Neural network layers; 7.5.3 Node calculations; 7.5.4 Neural network predictions; 7.5.5 Learning process; 7.5.6 Backpropagation; 7.5.7 Using neural networks; 7.5.8 Example
7.6 Other methods
Record Nr. UNINA-9910830132903321
Myatt Glenn J. <1969->  
Hoboken, N.J., : Wiley-Interscience, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Making sense of data : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt
Making sense of data : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt
Autore Myatt Glenn J. <1969->
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2007
Descrizione fisica 1 online resource (294 p.)
Disciplina 006.312
Soggetto topico Data mining
Mathematical statistics
ISBN 9786610721788
9781280721786
1280721782
9780470101025
0470101024
9780470101018
0470101016
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Making Sense of Data; Contents; Preface; 1. Introduction; 1.1 Overview; 1.2 Problem definition; 1.3 Data preparation; 1.4 Implementation of the analysis; 1.5 Deployment of the results; 1.6 Book outline; 1.7 Summary; 1.8 Further reading; 2. Definition; 2.1 Overview; 2.2 Objectives; 2.3 Deliverables; 2.4 Roles and responsibilities; 2.5 Project plan; 2.6 Case study; 2.6.1 Overview; 2.6.2 Problem; 2.6.3 Deliverables; 2.6.4 Roles and responsibilities; 2.6.5 Current situation; 2.6.6 Timetable and budget; 2.6.7 Cost/benefit analysis; 2.7 Summary; 2.8 Further reading; 3. Preparation; 3.1 Overview
3.2 Data sources3.3 Data understanding; 3.3.1 Data tables; 3.3.2 Continuous and discrete variables; 3.3.3 Scales of measurement; 3.3.4 Roles in analysis; 3.3.5 Frequency distribution; 3.4 Data preparation; 3.4.1 Overview; 3.4.2 Cleaning the data; 3.4.3 Removing variables; 3.4.4 Data transformations; 3.4.5 Segmentation; 3.5 Summary; 3.6 Exercises; 3.7 Further reading; 4. Tables and graphs; 4.1 Introduction; 4.2 Tables; 4.2.1 Data tables; 4.2.2 Contingency tables; 4.2.3 Summary tables; 4.3 Graphs; 4.3.1 Overview; 4.3.2 Frequency polygrams and histograms; 4.3.3 Scatterplots; 4.3.4 Box plots
4.3.5 Multiple graphs4.4 Summary; 4.5 Exercises; 4.6 Further reading; 5. Statistics; 5.1 Overview; 5.2 Descriptive statistics; 5.2.1 Overview; 5.2.2 Central tendency; 5.2.3 Variation; 5.2.4 Shape; 5.2.5 Example; 5.3 Inferential statistics; 5.3.1 Overview; 5.3.2 Confidence intervals; 5.3.3 Hypothesis tests; 5.3.4 Chi-square; 5.3.5 One-way analysis of variance; 5.4 Comparative statistics; 5.4.1 Overview; 5.4.2 Visualizing relationships; 5.4.3 Correlation coefficient (r); 5.4.4 Correlation analysis for more than two variables; 5.5 Summary; 5.6 Exercises; 5.7 Further reading; 6. Grouping
6.1 Introduction6.1.1 Overview; 6.1.2 Grouping by values or ranges; 6.1.3 Similarity measures; 6.1.4 Grouping approaches; 6.2 Clustering; 6.2.1 Overview; 6.2.2 Hierarchical agglomerative clustering; 6.2.3 K-means clustering; 6.3 Associative rules; 6.3.1 Overview; 6.3.2 Grouping by value combinations; 6.3.3 Extracting rules from groups; 6.3.4 Example; 6.4 Decision trees; 6.4.1 Overview; 6.4.2 Tree generation; 6.4.3 Splitting criteria; 6.4.4 Example; 6.5 Summary; 6.6 Exercises; 6.7 Further reading; 7. Prediction; 7.1 Introduction; 7.1.1 Overview; 7.1.2 Classification; 7.1.3 Regression
7.1.4 Building a prediction model7.1.5 Applying a prediction model; 7.2 Simple regression models; 7.2.1 Overview; 7.2.2 Simple linear regression; 7.2.3 Simple nonlinear regression; 7.3 K-nearest neighbors; 7.3.1 Overview; 7.3.2 Learning; 7.3.3 Prediction; 7.4 Classification and regression trees; 7.4.1 Overview; 7.4.2 Predicting using decision trees; 7.4.3 Example; 7.5 Neural networks; 7.5.1 Overview; 7.5.2 Neural network layers; 7.5.3 Node calculations; 7.5.4 Neural network predictions; 7.5.5 Learning process; 7.5.6 Backpropagation; 7.5.7 Using neural networks; 7.5.8 Example
7.6 Other methods
Altri titoli varianti A practical guide to exploratory data analysis and data mining
Record Nr. UNINA-9911019449203321
Myatt Glenn J. <1969->  
Hoboken, N.J., : Wiley-Interscience, c2007
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Making sense of data I : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt, Wayne P. Johnson
Making sense of data I : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt, Wayne P. Johnson
Autore Myatt Glenn J. <1969->
Edizione [Second edition.]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2014
Descrizione fisica 1 online resource (250 p.)
Disciplina 006.3/12
Soggetto topico Data mining
Mathematical statistics
ISBN 1-118-42200-7
1-118-42201-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Making Sense of Data I; Contents; Preface; 1 Introduction; 1.1 Overview; 1.2 Sources of Data; 1.3 Process for Making Sense of Data; 1.3.1 Overview; 1.3.2 Problem Definition and Planning; 1.3.3 Data Preparation; 1.3.4 Analysis; 1.3.5 Deployment; 1.4 OVERVIEW OF BOOK; 1.4.1 Describing Data; 1.4.2 Preparing Data Tables; 1.4.3 Understanding Relationships; 1.4.4 Understanding Groups; 1.4.5 Building Models; 1.4.6 Exercises; 1.4.7 Tutorials; 1.5 Summary; Further Reading; Exercises; Exercises; Exercises; Exercises; 2 Describing Data; 2.1 Overview; 2.2 Observations and Variables
2.3 Types of Variables2.4 Central Tendency; 2.4.1 Overview; 2.4.2 Mode; 2.4.3 Median; 2.4.4 Mean; 2.5 Distribution of the Data; 2.5.1 Overview; 2.5.2 Bar Charts and Frequency Histograms; 2.5.3 Range; 2.5.4 Quartiles; 2.5.5 Box Plots; 2.5.6 Variance; 2.5.7 Standard Deviation; 2.5.8 Shape; 2.6 Confidence Intervals; 2.7 Hypothesis Tests; Further Reading; Further Reading; Further Reading; Further Reading; 3 Preparing Data Tables; 3.1 Overview; 3.2 Cleaning the Data; 3.3 Removing Observations and Variables; 3.4 Generating Consistent Scales Across Variables; 3.5 New Frequency Distribution
3.6 Converting Text to Numbers3.7 Converting Continuous Data to Categories; 3.8 Combining Variables; 3.9 Generating Groups; 3.10 Preparing Unstructured Data; 4 Understanding Relationships; 4.1 Overview; 4.2 Visualizing Relationships Between Variables; 4.2.1 Scatterplots; 4.2.2 Summary Tables and Charts; 4.2.3 Cross-Classification Tables; 4.3 Calculating Metrics About Relationships; 4.3.1 Overview; 4.3.2 Correlation Coefficients; 4.3.3 Kendall Tau; 4.3.4 t-Tests Comparing Two Groups; 4.3.5 ANOVA; 4.3.6 Chi-Square; 5 Identifying and Understanding Groups; 5.1 Overview; 5.2 Clustering
5.2.1 Overview5.2.2 Distances; 5.2.3 Agglomerative Hierarchical Clustering; 5.2.4 k-Means Clustering; 5.3 Association Rules; 5.3.1 Overview; 5.3.2 Grouping by Combinations of Values; 5.3.3 Extracting and Assessing Rules; 5.3.4 Example; 5.4 Learning Decision Trees from Data; 5.4.1 Overview; 5.4.2 Splitting; 5.4.3 Splitting Criteria; 5.4.4 Example; Exercises; Further Reading; 6 Building Models from Data; 6.1 Overview; 6.2 Linear Regression; 6.2.1 Overview; 6.2.2 Fitting a Simple Linear Regression Model; 6.2.3 Fitting a Multiple Linear Regression Model; 6.2.4 Assessing the Model Fit
6.2.5 Testing Assumptions6.2.6 Selecting and Assessing Independent Variables; 6.3 Logistic Regression; 6.3.1 Overview; 6.3.2 Fitting a Simple Logistic Regression Model; 6.3.3 Fitting and Interpreting Multiple Logistic Regression Models; 6.3.4 Significance of Model and Coefficients; 6.3.5 Classification; 6.4 k-Nearest Neighbors; 6.4.1 Overview; 6.4.2 Training; 6.4.3 Predicting; 6.5 Classification and Regression Trees; 6.5.1 Overview; 6.5.2 Predicting; 6.5.3 Example; 6.6 Other Approaches; 6.6.1 Neural Networks; 6.6.2 Support Vector Machines; 6.6.3 Discriminant Analysis; 6.6.4 Naïve Bayes
6.6.5 Random Forests
Record Nr. UNINA-9910132172203321
Myatt Glenn J. <1969->  
Hoboken, New Jersey : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Making sense of data I : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt, Wayne P. Johnson
Making sense of data I : a practical guide to exploratory data analysis and data mining / / Glenn J. Myatt, Wayne P. Johnson
Autore Myatt Glenn J. <1969->
Edizione [Second edition.]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2014
Descrizione fisica 1 online resource (250 p.)
Disciplina 006.3/12
Soggetto topico Data mining
Mathematical statistics
ISBN 1-118-42200-7
1-118-42201-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Making Sense of Data I; Contents; Preface; 1 Introduction; 1.1 Overview; 1.2 Sources of Data; 1.3 Process for Making Sense of Data; 1.3.1 Overview; 1.3.2 Problem Definition and Planning; 1.3.3 Data Preparation; 1.3.4 Analysis; 1.3.5 Deployment; 1.4 OVERVIEW OF BOOK; 1.4.1 Describing Data; 1.4.2 Preparing Data Tables; 1.4.3 Understanding Relationships; 1.4.4 Understanding Groups; 1.4.5 Building Models; 1.4.6 Exercises; 1.4.7 Tutorials; 1.5 Summary; Further Reading; Exercises; Exercises; Exercises; Exercises; 2 Describing Data; 2.1 Overview; 2.2 Observations and Variables
2.3 Types of Variables2.4 Central Tendency; 2.4.1 Overview; 2.4.2 Mode; 2.4.3 Median; 2.4.4 Mean; 2.5 Distribution of the Data; 2.5.1 Overview; 2.5.2 Bar Charts and Frequency Histograms; 2.5.3 Range; 2.5.4 Quartiles; 2.5.5 Box Plots; 2.5.6 Variance; 2.5.7 Standard Deviation; 2.5.8 Shape; 2.6 Confidence Intervals; 2.7 Hypothesis Tests; Further Reading; Further Reading; Further Reading; Further Reading; 3 Preparing Data Tables; 3.1 Overview; 3.2 Cleaning the Data; 3.3 Removing Observations and Variables; 3.4 Generating Consistent Scales Across Variables; 3.5 New Frequency Distribution
3.6 Converting Text to Numbers3.7 Converting Continuous Data to Categories; 3.8 Combining Variables; 3.9 Generating Groups; 3.10 Preparing Unstructured Data; 4 Understanding Relationships; 4.1 Overview; 4.2 Visualizing Relationships Between Variables; 4.2.1 Scatterplots; 4.2.2 Summary Tables and Charts; 4.2.3 Cross-Classification Tables; 4.3 Calculating Metrics About Relationships; 4.3.1 Overview; 4.3.2 Correlation Coefficients; 4.3.3 Kendall Tau; 4.3.4 t-Tests Comparing Two Groups; 4.3.5 ANOVA; 4.3.6 Chi-Square; 5 Identifying and Understanding Groups; 5.1 Overview; 5.2 Clustering
5.2.1 Overview5.2.2 Distances; 5.2.3 Agglomerative Hierarchical Clustering; 5.2.4 k-Means Clustering; 5.3 Association Rules; 5.3.1 Overview; 5.3.2 Grouping by Combinations of Values; 5.3.3 Extracting and Assessing Rules; 5.3.4 Example; 5.4 Learning Decision Trees from Data; 5.4.1 Overview; 5.4.2 Splitting; 5.4.3 Splitting Criteria; 5.4.4 Example; Exercises; Further Reading; 6 Building Models from Data; 6.1 Overview; 6.2 Linear Regression; 6.2.1 Overview; 6.2.2 Fitting a Simple Linear Regression Model; 6.2.3 Fitting a Multiple Linear Regression Model; 6.2.4 Assessing the Model Fit
6.2.5 Testing Assumptions6.2.6 Selecting and Assessing Independent Variables; 6.3 Logistic Regression; 6.3.1 Overview; 6.3.2 Fitting a Simple Logistic Regression Model; 6.3.3 Fitting and Interpreting Multiple Logistic Regression Models; 6.3.4 Significance of Model and Coefficients; 6.3.5 Classification; 6.4 k-Nearest Neighbors; 6.4.1 Overview; 6.4.2 Training; 6.4.3 Predicting; 6.5 Classification and Regression Trees; 6.5.1 Overview; 6.5.2 Predicting; 6.5.3 Example; 6.6 Other Approaches; 6.6.1 Neural Networks; 6.6.2 Support Vector Machines; 6.6.3 Discriminant Analysis; 6.6.4 Naïve Bayes
6.6.5 Random Forests
Record Nr. UNINA-9910827866003321
Myatt Glenn J. <1969->  
Hoboken, New Jersey : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Making sense of data II [[electronic resource] ] : a practical guide to data visualization, advanced data mining methods, and applications / / Glenn J. Myatt, Wayne P. Johnson
Making sense of data II [[electronic resource] ] : a practical guide to data visualization, advanced data mining methods, and applications / / Glenn J. Myatt, Wayne P. Johnson
Autore Myatt Glenn J. <1969->
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, N.J., : John Wiley & Sons, c2009
Descrizione fisica 1 online resource (307 p.)
Disciplina 005.74
Altri autori (Persone) JohnsonWayne P
Soggetto topico Data mining
Information visualization
ISBN 1-282-03107-4
9786612031076
0-470-41740-4
0-470-41739-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto MAKING SENSE OF DATA II; CONTENTS; PREFACE; 1 INTRODUCTION; 1.1 Overview; 1.2 Definition; 1.3 Preparation; 1.3.1 Overview; 1.3.2 Accessing Tabular Data; 1.3.3 Accessing Unstructured Data; 1.3.4 Understanding the Variables and Observations; 1.3.5 Data Cleaning; 1.3.6 Transformation; 1.3.7 Variable Reduction; 1.3.8 Segmentation; 1.3.9 Preparing Data to Apply; 1.4 Analysis; 1.4.1 Data Mining Tasks; 1.4.2 Optimization; 1.4.3 Evaluation; 1.4.4 Model Forensics; 1.5 Deployment; 1.6 Outline of Book; 1.6.1 Overview; 1.6.2 Data Visualization; 1.6.3 Clustering; 1.6.4 Predictive Analytics
1.6.5 Applications1.6.6 Software; 1.7 Summary; 1.8 Further Reading; 2 DATA VISUALIZATION; 2.1 Overview; 2.2 Visualization Design Principles; 2.2.1 General Principles; 2.2.2 Graphics Design; 2.2.3 Anatomy of a Graph; 2.3 Tables; 2.3.1 Simple Tables; 2.3.2 Summary Tables; 2.3.3 Two-Way Contingency Tables; 2.3.4 Supertables; 2.4 Univariate Data Visualization; 2.4.1 Bar Chart; 2.4.2 Histograms; 2.4.3 Frequency Polygram; 2.4.4 Box Plots; 2.4.5 Dot Plot; 2.4.6 Stem-and-Leaf Plot; 2.4.7 Quantile Plot; 2.4.8 Quantile-Quantile Plot; 2.5 Bivariate Data Visualization; 2.5.1 Scatterplot
2.6 Multivariate Data Visualization2.6.1 Histogram Matrix; 2.6.2 Scatterplot Matrix; 2.6.3 Multiple Box Plot; 2.6.4 Trellis Plot; 2.7 Visualizing Groups; 2.7.1 Dendrograms; 2.7.2 Decision Trees; 2.7.3 Cluster Image Maps; 2.8 Dynamic Techniques; 2.8.1 Overview; 2.8.2 Data Brushing; 2.8.3 Nearness Selection; 2.8.4 Sorting and Rearranging; 2.8.5 Searching and Filtering; 2.9 Summary; 2.10 Further Reading; 3 CLUSTERING; 3.1 Overview; 3.2 Distance Measures; 3.2.1 Overview; 3.2.2 Numeric Distance Measures; 3.2.3 Binary Distance Measures; 3.2.4 Mixed Variables; 3.2.5 Other Measures
3.3 Agglomerative Hierarchical Clustering3.3.1 Overview; 3.3.2 Single Linkage; 3.3.3 Complete Linkage; 3.3.4 Average Linkage; 3.3.5 Other Methods; 3.3.6 Selecting Groups; 3.4 Partitioned-Based Clustering; 3.4.1 Overview; 3.4.2 k-Means; 3.4.3 Worked Example; 3.4.4 Miscellaneous Partitioned-Based Clustering; 3.5 Fuzzy Clustering; 3.5.1 Overview; 3.5.2 Fuzzy k-Means; 3.5.3 Worked Examples; 3.6 Summary; 3.7 Further Reading; 4 PREDICTIVE ANALYTICS; 4.1 Overview; 4.1.1 Predictive Modeling; 4.1.2 Testing Model Accuracy; 4.1.3 Evaluating Regression Models' Predictive Accuracy
4.1.4 Evaluating Classification Models' Predictive Accuracy4.1.5 Evaluating Binary Models' Predictive Accuracy; 4.1.6 ROC Charts; 4.1.7 Lift Chart; 4.2 Principal Component Analysis; 4.2.1 Overview; 4.2.2 Principal Components; 4.2.3 Generating Principal Components; 4.2.4 Interpretation of Principal Components; 4.3 Multiple Linear Regression; 4.3.1 Overview; 4.3.2 Generating Models; 4.3.3 Prediction; 4.3.4 Analysis of Residuals; 4.3.5 Standard Error; 4.3.6 Coefficient of Multiple Determination; 4.3.7 Testing the Model Significance; 4.3.8 Selecting and Transforming Variables
4.4 Discriminant Analysis
Record Nr. UNINA-9910145958103321
Myatt Glenn J. <1969->  
Hoboken, N.J., : John Wiley & Sons, c2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Making sense of data II : a practical guide to data visualization, advanced data mining methods, and applications / / Glenn J. Myatt, Wayne P. Johnson
Making sense of data II : a practical guide to data visualization, advanced data mining methods, and applications / / Glenn J. Myatt, Wayne P. Johnson
Autore Myatt Glenn J. <1969->
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, N.J., : John Wiley & Sons, c2009
Descrizione fisica 1 online resource (307 p.)
Disciplina 005.74
Altri autori (Persone) JohnsonWayne P
Soggetto topico Data mining
Information visualization
ISBN 9786612031076
9781282031074
1282031074
9780470417409
0470417404
9780470417393
0470417390
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto MAKING SENSE OF DATA II; CONTENTS; PREFACE; 1 INTRODUCTION; 1.1 Overview; 1.2 Definition; 1.3 Preparation; 1.3.1 Overview; 1.3.2 Accessing Tabular Data; 1.3.3 Accessing Unstructured Data; 1.3.4 Understanding the Variables and Observations; 1.3.5 Data Cleaning; 1.3.6 Transformation; 1.3.7 Variable Reduction; 1.3.8 Segmentation; 1.3.9 Preparing Data to Apply; 1.4 Analysis; 1.4.1 Data Mining Tasks; 1.4.2 Optimization; 1.4.3 Evaluation; 1.4.4 Model Forensics; 1.5 Deployment; 1.6 Outline of Book; 1.6.1 Overview; 1.6.2 Data Visualization; 1.6.3 Clustering; 1.6.4 Predictive Analytics
1.6.5 Applications1.6.6 Software; 1.7 Summary; 1.8 Further Reading; 2 DATA VISUALIZATION; 2.1 Overview; 2.2 Visualization Design Principles; 2.2.1 General Principles; 2.2.2 Graphics Design; 2.2.3 Anatomy of a Graph; 2.3 Tables; 2.3.1 Simple Tables; 2.3.2 Summary Tables; 2.3.3 Two-Way Contingency Tables; 2.3.4 Supertables; 2.4 Univariate Data Visualization; 2.4.1 Bar Chart; 2.4.2 Histograms; 2.4.3 Frequency Polygram; 2.4.4 Box Plots; 2.4.5 Dot Plot; 2.4.6 Stem-and-Leaf Plot; 2.4.7 Quantile Plot; 2.4.8 Quantile-Quantile Plot; 2.5 Bivariate Data Visualization; 2.5.1 Scatterplot
2.6 Multivariate Data Visualization2.6.1 Histogram Matrix; 2.6.2 Scatterplot Matrix; 2.6.3 Multiple Box Plot; 2.6.4 Trellis Plot; 2.7 Visualizing Groups; 2.7.1 Dendrograms; 2.7.2 Decision Trees; 2.7.3 Cluster Image Maps; 2.8 Dynamic Techniques; 2.8.1 Overview; 2.8.2 Data Brushing; 2.8.3 Nearness Selection; 2.8.4 Sorting and Rearranging; 2.8.5 Searching and Filtering; 2.9 Summary; 2.10 Further Reading; 3 CLUSTERING; 3.1 Overview; 3.2 Distance Measures; 3.2.1 Overview; 3.2.2 Numeric Distance Measures; 3.2.3 Binary Distance Measures; 3.2.4 Mixed Variables; 3.2.5 Other Measures
3.3 Agglomerative Hierarchical Clustering3.3.1 Overview; 3.3.2 Single Linkage; 3.3.3 Complete Linkage; 3.3.4 Average Linkage; 3.3.5 Other Methods; 3.3.6 Selecting Groups; 3.4 Partitioned-Based Clustering; 3.4.1 Overview; 3.4.2 k-Means; 3.4.3 Worked Example; 3.4.4 Miscellaneous Partitioned-Based Clustering; 3.5 Fuzzy Clustering; 3.5.1 Overview; 3.5.2 Fuzzy k-Means; 3.5.3 Worked Examples; 3.6 Summary; 3.7 Further Reading; 4 PREDICTIVE ANALYTICS; 4.1 Overview; 4.1.1 Predictive Modeling; 4.1.2 Testing Model Accuracy; 4.1.3 Evaluating Regression Models' Predictive Accuracy
4.1.4 Evaluating Classification Models' Predictive Accuracy4.1.5 Evaluating Binary Models' Predictive Accuracy; 4.1.6 ROC Charts; 4.1.7 Lift Chart; 4.2 Principal Component Analysis; 4.2.1 Overview; 4.2.2 Principal Components; 4.2.3 Generating Principal Components; 4.2.4 Interpretation of Principal Components; 4.3 Multiple Linear Regression; 4.3.1 Overview; 4.3.2 Generating Models; 4.3.3 Prediction; 4.3.4 Analysis of Residuals; 4.3.5 Standard Error; 4.3.6 Coefficient of Multiple Determination; 4.3.7 Testing the Model Significance; 4.3.8 Selecting and Transforming Variables
4.4 Discriminant Analysis
Altri titoli varianti Making sense of data 2
Making sense of data two
Record Nr. UNINA-9910826809903321
Myatt Glenn J. <1969->  
Hoboken, N.J., : John Wiley & Sons, c2009
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Making sense of data III [[electronic resource] ] : a practical guide to designing interactive data visualizations / / Glenn J. Myatt, Wayne P. Johnson
Making sense of data III [[electronic resource] ] : a practical guide to designing interactive data visualizations / / Glenn J. Myatt, Wayne P. Johnson
Autore Myatt Glenn J. <1969->
Pubbl/distr/stampa Hoboken, N.J., : Wiley, c2012
Descrizione fisica 1 online resource (400 p.)
Disciplina 006.3/12
Altri autori (Persone) JohnsonWayne P
Soggetto topico Data mining
Information visualization
ISBN 1-283-25796-3
9786613257963
1-118-12160-0
1-118-12161-9
1-118-12158-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Making Sense of Data III: A Practical Guide to Designing Interactive Data Visualizations; Contents; Preface; Acknowledgments; 1: Introduction; 1.1: Overview; 1.2: Visual Perception; 1.3: Visualization; 1.4: Designing for High-Throughput Data Exploration; 1.4.1: The IA (Intelligence Amplified) System; 1.4.2: Design; 1.4.3: Data; 1.5: Summary; 1.6: Further Reading; 2: The Cognitive and Visual Systems; 2.1: External Representations; 2.2: The Cognitive System; 2.2.1: The Matter of Thought; 2.2.2: Mental Processes and Internal Representations; 2.3: Visual Perception
2.3.1: The Problem of Scene Recognition2.3.2: Levels of Explanation; 2.3.3: Illuminating the Environment; 2.3.4: The Eye and Visual Pathways; 2.3.5: Processing the Retinal Image; 2.3.6: Color; 2.4: Influencing Visual Perception; 2.4.1: Eye Movements; 2.4.2: Attention; 2.4.3: Memory; 2.5: Summary; 2.6: Further Reading; 3: Graphic Representations; 3.1: Jacques Bertin: Semiology of Graphics; 3.1.1: The Essence of Semiotics; 3.1.2: The Properties and Structure of the Information; 3.1.3: The Properties of the Graphics System; 3.1.4: Constructing Efficient Graphics
3.2: Wilkinson: Grammar of Graphics3.2.1: The Graphic Pipeline; 3.2.2: The Graphic Specification; 3.2.3: Components of the Grammar; 3.3: Wickham: ggplot2; 3.3.1: The Graphic Pipeline; 3.3.2: The Graphic Specification and Components; 3.4: Bostock and Heer: Protovis; 3.5: Summary; 3.6: Further Reading; 4: Designing Visual Interactions; 4.1: Designing for Complexity; 4.2: The Process of Design; 4.2.1: Analyze; 4.2.2: Design; 4.2.3: Prototype; 4.2.4: Evaluate; 4.3: Visual Interaction Design; 4.3.1: Visual Interfaces; 4.3.2: Visualizations; 4.3.3: Graphics; 4.3.4: Real-Time Constraints
4.4: Summary4.5: Further Reading; 5: Hands-On: Creating Interactive Visualizations with Protovis; 5.1: Using Protovis; 5.1.1: Overview; 5.1.2: Getting Started; 5.1.3: Chapter Overview; 5.1.4: Exercise; 5.2: Creating Code Using the Protovis Graphical Framework; 5.2.1: Overview; 5.2.2: Panels; 5.2.3: Marks; 5.2.4: Using Functions; 5.2.5: Variables; 5.2.6: Exercises; 5.3: Basic Protovis Marks; 5.3.1: Bar; 5.3.2: Label; 5.3.3: Dot; 5.3.4: Line; 5.3.5: Area; 5.3.6: Wedge; 5.3.7: Images; 5.3.8: Exercises; 5.4: Creating Customized Plots; 5.4.1: Colors; 5.4.2: Formatting; 5.4.3: Anchors; 5.4.4: Rule
5.4.5: Scales5.4.6: Exercises; 5.5: Creating Basic Plots; 5.5.1: Overview; 5.5.2: Handling Arrays and Data; 5.5.3: Reading Data from Files; 5.5.4: Worked Examples; 5.5.5: Exercises; 5.6: Data Graphics; 5.6.1: Frequency Histograms; 5.6.2: Box-and-Whisker Plots; 5.6.3: Scatterplots; 5.6.4: Exercises; 5.7: Composite Plots; 5.7.1: Creating Grouped Plots Using Multiple Panels; 5.7.2: Inheritance; 5.7.3: Property Chaining; 5.7.4: Creating Plot Matrices Using Multiple Panels; 5.7.5: Layout Management; 5.7.6: Networks; 5.7.7: Hierarchies; 5.7.8: Sparklines; 5.7.9: Exercises; 5.8: Interactive Plots
5.8.1: Overview
Record Nr. UNINA-9910139608103321
Myatt Glenn J. <1969->  
Hoboken, N.J., : Wiley, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Making sense of data III : a practical guide to designing interactive data visualizations / / Glenn J. Myatt, Wayne P. Johnson
Making sense of data III : a practical guide to designing interactive data visualizations / / Glenn J. Myatt, Wayne P. Johnson
Autore Myatt Glenn J. <1969->
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, c2012
Descrizione fisica 1 online resource (400 p.)
Disciplina 006.3/12
Altri autori (Persone) JohnsonWayne P
Soggetto topico Data mining
Information visualization
ISBN 9786613257963
9781283257961
1283257963
9781118121603
1118121600
9781118121610
1118121619
9781118121580
1118121589
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Making Sense of Data III: A Practical Guide to Designing Interactive Data Visualizations; Contents; Preface; Acknowledgments; 1: Introduction; 1.1: Overview; 1.2: Visual Perception; 1.3: Visualization; 1.4: Designing for High-Throughput Data Exploration; 1.4.1: The IA (Intelligence Amplified) System; 1.4.2: Design; 1.4.3: Data; 1.5: Summary; 1.6: Further Reading; 2: The Cognitive and Visual Systems; 2.1: External Representations; 2.2: The Cognitive System; 2.2.1: The Matter of Thought; 2.2.2: Mental Processes and Internal Representations; 2.3: Visual Perception
2.3.1: The Problem of Scene Recognition2.3.2: Levels of Explanation; 2.3.3: Illuminating the Environment; 2.3.4: The Eye and Visual Pathways; 2.3.5: Processing the Retinal Image; 2.3.6: Color; 2.4: Influencing Visual Perception; 2.4.1: Eye Movements; 2.4.2: Attention; 2.4.3: Memory; 2.5: Summary; 2.6: Further Reading; 3: Graphic Representations; 3.1: Jacques Bertin: Semiology of Graphics; 3.1.1: The Essence of Semiotics; 3.1.2: The Properties and Structure of the Information; 3.1.3: The Properties of the Graphics System; 3.1.4: Constructing Efficient Graphics
3.2: Wilkinson: Grammar of Graphics3.2.1: The Graphic Pipeline; 3.2.2: The Graphic Specification; 3.2.3: Components of the Grammar; 3.3: Wickham: ggplot2; 3.3.1: The Graphic Pipeline; 3.3.2: The Graphic Specification and Components; 3.4: Bostock and Heer: Protovis; 3.5: Summary; 3.6: Further Reading; 4: Designing Visual Interactions; 4.1: Designing for Complexity; 4.2: The Process of Design; 4.2.1: Analyze; 4.2.2: Design; 4.2.3: Prototype; 4.2.4: Evaluate; 4.3: Visual Interaction Design; 4.3.1: Visual Interfaces; 4.3.2: Visualizations; 4.3.3: Graphics; 4.3.4: Real-Time Constraints
4.4: Summary4.5: Further Reading; 5: Hands-On: Creating Interactive Visualizations with Protovis; 5.1: Using Protovis; 5.1.1: Overview; 5.1.2: Getting Started; 5.1.3: Chapter Overview; 5.1.4: Exercise; 5.2: Creating Code Using the Protovis Graphical Framework; 5.2.1: Overview; 5.2.2: Panels; 5.2.3: Marks; 5.2.4: Using Functions; 5.2.5: Variables; 5.2.6: Exercises; 5.3: Basic Protovis Marks; 5.3.1: Bar; 5.3.2: Label; 5.3.3: Dot; 5.3.4: Line; 5.3.5: Area; 5.3.6: Wedge; 5.3.7: Images; 5.3.8: Exercises; 5.4: Creating Customized Plots; 5.4.1: Colors; 5.4.2: Formatting; 5.4.3: Anchors; 5.4.4: Rule
5.4.5: Scales5.4.6: Exercises; 5.5: Creating Basic Plots; 5.5.1: Overview; 5.5.2: Handling Arrays and Data; 5.5.3: Reading Data from Files; 5.5.4: Worked Examples; 5.5.5: Exercises; 5.6: Data Graphics; 5.6.1: Frequency Histograms; 5.6.2: Box-and-Whisker Plots; 5.6.3: Scatterplots; 5.6.4: Exercises; 5.7: Composite Plots; 5.7.1: Creating Grouped Plots Using Multiple Panels; 5.7.2: Inheritance; 5.7.3: Property Chaining; 5.7.4: Creating Plot Matrices Using Multiple Panels; 5.7.5: Layout Management; 5.7.6: Networks; 5.7.7: Hierarchies; 5.7.8: Sparklines; 5.7.9: Exercises; 5.8: Interactive Plots
5.8.1: Overview
Record Nr. UNINA-9910809871103321
Myatt Glenn J. <1969->  
Hoboken, N.J., : Wiley, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui