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Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose
| Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose |
| Autore | Larose Daniel T. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2015 |
| Descrizione fisica | 1 online resource (827 p.) |
| Disciplina | 006.3/12 |
| Collana | Wiley Series on Methods and Applications in Data Mining |
| Soggetto topico |
Data mining
Prediction theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-118-86870-6
1-118-86867-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Contents; Preface; Acknowledgments; Part I Data Preparation; Chapter 1 An Introduction to Data Mining and Predictive Analytics; 1.1 What is Data Mining? What is Predictive Analytics?; 1.2 Wanted: Data Miners; 1.3 The Need for Human Direction of Data Mining; 1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM; 1.4.1 CRISP-DM: The Six Phases; 1.5 Fallacies of Data Mining; 1.6 What Tasks Can Data Mining Accomplish; 1.6.1 Description; 1.6.2 Estimation; 1.6.3 Prediction; 1.6.4 Classification; 1.6.5 Clustering; 1.6.6 Association; The R Zone; R References; Exercises
Chapter 2 Data Preprocessing2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data; 2.4 Identifying Misclassifications; 2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables 2.17 Adding an Index Field2.18 Removing Variables that are not Useful; 2.19 Variables that Should Probably not be Removed; 2.20 Removal of Duplicate Records; 2.21 A Word About ID Fields; The R Zone; R Reference; Exercises; Chapter 3 Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know the Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields 3.8 Binning Based on Predictive Value3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables; 3.11 Using EDA to Investigate Correlated Predictor Variables; 3.12 Summary of Our EDA; The R Zone; R References; Exercises; Chapter 4 Dimension-Reduction Methods; 4.1 Need for Dimension-Reduction in Data Mining; 4.2 Principal Components Analysis; 4.3 Applying PCA to the Houses Data Set; 4.4 How Many Components Should We Extract?; 4.4.1 The Eigenvalue Criterion; 4.4.2 The Proportion of Variance Explained Criterion; 4.4.3 The Minimum Communality Criterion 4.4.4 The Scree Plot Criterion4.5 Profiling the Principal Components; 4.6 Communalities; 4.6.1 Minimum Communality Criterion; 4.7 Validation of the Principal Components; 4.8 Factor Analysis; 4.9 Applying Factor Analysis to the Adult Data Set; 4.10 Factor Rotation; 4.11 User-Defined Composites; 4.12 An Example of a User-Defined Composite; The R Zone; R References; Exercises; Part II Statistical Analysis; Chapter 5 Univariate Statistical Analysis; 5.1 Data Mining Tasks in Discovering Knowledge in Data; 5.2 Statistical Approaches to Estimation and Prediction; 5.3 Statistical Inference 5.4 How Confident are We in Our Estimates? |
| Record Nr. | UNINA-9910460169903321 |
Larose Daniel T.
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose
| Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose |
| Autore | Larose Daniel T. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2015 |
| Descrizione fisica | 1 online resource (824 pages) : illustrations |
| Disciplina | 006.3/12 |
| Collana | Wiley Series on Methods and Applications in Data Mining |
| Soggetto topico |
Data mining
Prediction theory |
| ISBN |
1-118-86867-6
1-118-86870-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Series; Title Page; Copyright; Table of Contents; Dedication; Preface; What is Data Mining? What is Predictive Analytics?; Why is this Book Needed?; Who Will Benefit from this Book?; Danger! Data Mining is Easy to do Badly; "White-Box" Approach; Algorithm Walk-Throughs; Exciting New Topics; The R Zone; Appendix: Data Summarization and Visualization; The Case Study: Bringing it all Together; How the Book is Structured; The Software; Weka: The Open-Source Alternative; The Companion Web Site: www.dataminingconsultant.com; Data Mining and Predictive Analytics as a Textbook; Acknowledgments. Daniel's AcknowledgmentsChantal's Acknowledgments; Part I: Data Preparation; Chapter 1: An Introduction to Data Mining and Predictive Analytics; 1.1 What is Data Mining? What Is Predictive Analytics?; 1.2 Wanted: Data Miners; 1.3 The Need For Human Direction of Data Mining; 1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM; 1.5 Fallacies of Data Mining; 1.6 What Tasks can Data Mining Accomplish; The R Zone; R References; Exercises; Chapter 2: Data Preprocessing; 2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data. 2.4 Identifying Misclassifications2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables; 2.17 Adding an Index Field; 2.18 Removing Variables that are not Useful; 2.19 Variables that Should Probably not be Removed. 2.20 Removal of Duplicate Records2.21 A Word About ID Fields; The R Zone; R Reference; Exercises; Chapter 3: Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know The Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields; 3.8 Binning Based on Predictive Value; 3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables. 3.11 Using EDA to Investigate Correlated Predictor Variables3.12 Summary of Our EDA; The R Zone; R References; Exercises; Chapter 4: Dimension-Reduction Methods; 4.1 Need for Dimension-Reduction in Data Mining; 4.2 Principal Components Analysis; 4.3 Applying PCA to the Houses Data Set; 4.4 How Many Components Should We Extract?; 4.5 Profiling the Principal Components; 4.6 Communalities; 4.7 Validation of the Principal Components; 4.8 Factor Analysis; 4.9 Applying Factor Analysis to the Adult Data Set; 4.10 Factor Rotation; 4.11 User-Defined Composites. |
| Record Nr. | UNINA-9910795970103321 |
|
Larose Daniel T.
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose
| Data mining and predictive analytics / / Daniel T. Larose, Chantal D. Larose |
| Autore | Larose Daniel T. |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2015 |
| Descrizione fisica | 1 online resource (824 pages) : illustrations |
| Disciplina | 006.3/12 |
| Collana | Wiley Series on Methods and Applications in Data Mining |
| Soggetto topico |
Data mining
Prediction theory |
| ISBN |
1-118-86867-6
1-118-86870-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Series; Title Page; Copyright; Table of Contents; Dedication; Preface; What is Data Mining? What is Predictive Analytics?; Why is this Book Needed?; Who Will Benefit from this Book?; Danger! Data Mining is Easy to do Badly; "White-Box" Approach; Algorithm Walk-Throughs; Exciting New Topics; The R Zone; Appendix: Data Summarization and Visualization; The Case Study: Bringing it all Together; How the Book is Structured; The Software; Weka: The Open-Source Alternative; The Companion Web Site: www.dataminingconsultant.com; Data Mining and Predictive Analytics as a Textbook; Acknowledgments. Daniel's AcknowledgmentsChantal's Acknowledgments; Part I: Data Preparation; Chapter 1: An Introduction to Data Mining and Predictive Analytics; 1.1 What is Data Mining? What Is Predictive Analytics?; 1.2 Wanted: Data Miners; 1.3 The Need For Human Direction of Data Mining; 1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM; 1.5 Fallacies of Data Mining; 1.6 What Tasks can Data Mining Accomplish; The R Zone; R References; Exercises; Chapter 2: Data Preprocessing; 2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data. 2.4 Identifying Misclassifications2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables; 2.17 Adding an Index Field; 2.18 Removing Variables that are not Useful; 2.19 Variables that Should Probably not be Removed. 2.20 Removal of Duplicate Records2.21 A Word About ID Fields; The R Zone; R Reference; Exercises; Chapter 3: Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know The Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields; 3.8 Binning Based on Predictive Value; 3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables. 3.11 Using EDA to Investigate Correlated Predictor Variables3.12 Summary of Our EDA; The R Zone; R References; Exercises; Chapter 4: Dimension-Reduction Methods; 4.1 Need for Dimension-Reduction in Data Mining; 4.2 Principal Components Analysis; 4.3 Applying PCA to the Houses Data Set; 4.4 How Many Components Should We Extract?; 4.5 Profiling the Principal Components; 4.6 Communalities; 4.7 Validation of the Principal Components; 4.8 Factor Analysis; 4.9 Applying Factor Analysis to the Adult Data Set; 4.10 Factor Rotation; 4.11 User-Defined Composites. |
| Record Nr. | UNINA-9910807887903321 |
|
Larose Daniel T.
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining methods and models / / Daniel T. Larose
| Data mining methods and models / / Daniel T. Larose |
| Autore | Larose Daniel T. |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley-Interscience, , c2006 |
| Descrizione fisica | 1 online resource (340 p.) |
| Disciplina | 005.74 |
| Soggetto topico | Data mining |
| ISBN |
1-280-31166-5
9786610311668 0-470-35545-X 0-471-75648-2 0-471-75647-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Dimension reduction methods -- Regression modeling -- Multiple regression and model building -- Logistic regression -- Naïve Bayes estimation and Bayesian networks -- Genetic algorithms -- Case study : modeling response to direct mail marketing. |
| Record Nr. | UNINA-9910143573303321 |
|
Larose Daniel T.
|
||
| Hoboken, New Jersey : , : Wiley-Interscience, , c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Discovering knowledge in data : an introduction to data mining / / Daniel T. Larose, Chantal D. Larose
| Discovering knowledge in data : an introduction to data mining / / Daniel T. Larose, Chantal D. Larose |
| Autore | Larose Daniel T. |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : IEEE, , 2014 |
| Descrizione fisica | 1 online resource (336 p.) |
| Disciplina | 006.3/12 |
| Collana | Wiley Series on Methods and Applications in Data Mining |
| Soggetto topico | Data mining |
| ISBN |
1-118-87357-2
1-118-87405-6 1-118-87358-0 |
| Classificazione | COM021040COM021030 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
DISCOVERING KNOWLEDGE IN DATA; Contents; Preface; 1 An Introduction to Data Mining; 1.1 What is Data Mining?; 1.2 Wanted: Data Miners; 1.3 The Need for Human Direction of Data Mining; 1.4 The Cross-Industry Standard Practice for Data Mining; 1.4.1 Crisp-DM: The Six Phases; 1.5 Fallacies of Data Mining; 1.6 What Tasks Can Data Mining Accomplish?; 1.6.1 Description; 1.6.2 Estimation; 1.6.3 Prediction; 1.6.4 Classification; 1.6.5 Clustering; 1.6.6 Association; References; Exercises; 2 Data Preprocessing; 2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data
2.4 Identifying Misclassifications2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables; 2.17 Adding an Index Field; 2.18 Removing Variables that are Not Useful; 2.19 Variables that Should Probably Not Be Removed 2.20 Removal of Duplicate Records2.21 A Word About Id Fields; THE R ZONE; References; Exercises; Hands-On Analysis; 3 Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know the Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields; 3.8 Binning Based on Predictive Value; 3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables 3.11 Using EDA to Investigate Correlated Predictor Variables3.12 Summary; THE R ZONE; Reference; Exercises; Hands-On Analysis; 4 Univariate Statistical Analysis; 4.1 Data Mining Tasks in Discovering Knowledge in Data; 4.2 Statistical Approaches to Estimation and Prediction; 4.3 Statistical Inference; 4.4 How Confident are We in Our Estimates?; 4.5 Confidence Interval Estimation of the Mean; 4.6 How to Reduce the Margin of Error; 4.7 Confidence Interval Estimation of the Proportion; 4.8 Hypothesis Testing for the Mean; 4.9 Assessing the Strength of Evidence Against the Null Hypothesis 4.10 Using Confidence Intervals to Perform Hypothesis Tests4.11 Hypothesis Testing for the Proportion; THE R ZONE; Reference; Exercises; 5 Multivariate Statistics; 5.1 Two-Sample t-Test for Difference in Means; 5.2 Two-Sample Z-Test for Difference in Proportions; 5.3 Test for Homogeneity of Proportions; 5.4 Chi-Square Test for Goodness of Fit of Multinomial Data; 5.5 Analysis of Variance; 5.6 Regression Analysis; 5.7 Hypothesis Testing in Regression; 5.8 Measuring the Quality of a Regression Model; 5.9 Dangers of Extrapolation; 5.10 Confidence Intervals for the Mean Value of Given 5.11 Prediction Intervals for a Randomly Chosen Value of Given |
| Record Nr. | UNISA-996198490203316 |
|
Larose Daniel T.
|
||
| Hoboken, New Jersey : , : IEEE, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Discovering knowledge in data : an introduction to data mining / / Daniel T. Larose, Chantal D. Larose
| Discovering knowledge in data : an introduction to data mining / / Daniel T. Larose, Chantal D. Larose |
| Autore | Larose Daniel T. |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : IEEE, , 2014 |
| Descrizione fisica | 1 online resource (336 p.) |
| Disciplina | 006.3/12 |
| Collana | Wiley Series on Methods and Applications in Data Mining |
| Soggetto topico | Data mining |
| ISBN |
1-118-87357-2
1-118-87405-6 1-118-87358-0 |
| Classificazione | COM021040COM021030 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
DISCOVERING KNOWLEDGE IN DATA; Contents; Preface; 1 An Introduction to Data Mining; 1.1 What is Data Mining?; 1.2 Wanted: Data Miners; 1.3 The Need for Human Direction of Data Mining; 1.4 The Cross-Industry Standard Practice for Data Mining; 1.4.1 Crisp-DM: The Six Phases; 1.5 Fallacies of Data Mining; 1.6 What Tasks Can Data Mining Accomplish?; 1.6.1 Description; 1.6.2 Estimation; 1.6.3 Prediction; 1.6.4 Classification; 1.6.5 Clustering; 1.6.6 Association; References; Exercises; 2 Data Preprocessing; 2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data
2.4 Identifying Misclassifications2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables; 2.17 Adding an Index Field; 2.18 Removing Variables that are Not Useful; 2.19 Variables that Should Probably Not Be Removed 2.20 Removal of Duplicate Records2.21 A Word About Id Fields; THE R ZONE; References; Exercises; Hands-On Analysis; 3 Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know the Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields; 3.8 Binning Based on Predictive Value; 3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables 3.11 Using EDA to Investigate Correlated Predictor Variables3.12 Summary; THE R ZONE; Reference; Exercises; Hands-On Analysis; 4 Univariate Statistical Analysis; 4.1 Data Mining Tasks in Discovering Knowledge in Data; 4.2 Statistical Approaches to Estimation and Prediction; 4.3 Statistical Inference; 4.4 How Confident are We in Our Estimates?; 4.5 Confidence Interval Estimation of the Mean; 4.6 How to Reduce the Margin of Error; 4.7 Confidence Interval Estimation of the Proportion; 4.8 Hypothesis Testing for the Mean; 4.9 Assessing the Strength of Evidence Against the Null Hypothesis 4.10 Using Confidence Intervals to Perform Hypothesis Tests4.11 Hypothesis Testing for the Proportion; THE R ZONE; Reference; Exercises; 5 Multivariate Statistics; 5.1 Two-Sample t-Test for Difference in Means; 5.2 Two-Sample Z-Test for Difference in Proportions; 5.3 Test for Homogeneity of Proportions; 5.4 Chi-Square Test for Goodness of Fit of Multinomial Data; 5.5 Analysis of Variance; 5.6 Regression Analysis; 5.7 Hypothesis Testing in Regression; 5.8 Measuring the Quality of a Regression Model; 5.9 Dangers of Extrapolation; 5.10 Confidence Intervals for the Mean Value of Given 5.11 Prediction Intervals for a Randomly Chosen Value of Given |
| Record Nr. | UNINA-9910132206803321 |
|
Larose Daniel T.
|
||
| Hoboken, New Jersey : , : IEEE, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Discovering knowledge in data : an introduction to data mining / / Daniel T. Larose, Chantal D. Larose
| Discovering knowledge in data : an introduction to data mining / / Daniel T. Larose, Chantal D. Larose |
| Autore | Larose Daniel T. |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : IEEE, , 2014 |
| Descrizione fisica | 1 online resource (336 p.) |
| Disciplina | 006.3/12 |
| Collana | Wiley Series on Methods and Applications in Data Mining |
| Soggetto topico | Data mining |
| ISBN |
1-118-87357-2
1-118-87405-6 1-118-87358-0 |
| Classificazione | COM021040COM021030 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
DISCOVERING KNOWLEDGE IN DATA; Contents; Preface; 1 An Introduction to Data Mining; 1.1 What is Data Mining?; 1.2 Wanted: Data Miners; 1.3 The Need for Human Direction of Data Mining; 1.4 The Cross-Industry Standard Practice for Data Mining; 1.4.1 Crisp-DM: The Six Phases; 1.5 Fallacies of Data Mining; 1.6 What Tasks Can Data Mining Accomplish?; 1.6.1 Description; 1.6.2 Estimation; 1.6.3 Prediction; 1.6.4 Classification; 1.6.5 Clustering; 1.6.6 Association; References; Exercises; 2 Data Preprocessing; 2.1 Why do We Need to Preprocess the Data?; 2.2 Data Cleaning; 2.3 Handling Missing Data
2.4 Identifying Misclassifications2.5 Graphical Methods for Identifying Outliers; 2.6 Measures of Center and Spread; 2.7 Data Transformation; 2.8 Min-Max Normalization; 2.9 Z-Score Standardization; 2.10 Decimal Scaling; 2.11 Transformations to Achieve Normality; 2.12 Numerical Methods for Identifying Outliers; 2.13 Flag Variables; 2.14 Transforming Categorical Variables into Numerical Variables; 2.15 Binning Numerical Variables; 2.16 Reclassifying Categorical Variables; 2.17 Adding an Index Field; 2.18 Removing Variables that are Not Useful; 2.19 Variables that Should Probably Not Be Removed 2.20 Removal of Duplicate Records2.21 A Word About Id Fields; THE R ZONE; References; Exercises; Hands-On Analysis; 3 Exploratory Data Analysis; 3.1 Hypothesis Testing Versus Exploratory Data Analysis; 3.2 Getting to Know the Data Set; 3.3 Exploring Categorical Variables; 3.4 Exploring Numeric Variables; 3.5 Exploring Multivariate Relationships; 3.6 Selecting Interesting Subsets of the Data for Further Investigation; 3.7 Using EDA to Uncover Anomalous Fields; 3.8 Binning Based on Predictive Value; 3.9 Deriving New Variables: Flag Variables; 3.10 Deriving New Variables: Numerical Variables 3.11 Using EDA to Investigate Correlated Predictor Variables3.12 Summary; THE R ZONE; Reference; Exercises; Hands-On Analysis; 4 Univariate Statistical Analysis; 4.1 Data Mining Tasks in Discovering Knowledge in Data; 4.2 Statistical Approaches to Estimation and Prediction; 4.3 Statistical Inference; 4.4 How Confident are We in Our Estimates?; 4.5 Confidence Interval Estimation of the Mean; 4.6 How to Reduce the Margin of Error; 4.7 Confidence Interval Estimation of the Proportion; 4.8 Hypothesis Testing for the Mean; 4.9 Assessing the Strength of Evidence Against the Null Hypothesis 4.10 Using Confidence Intervals to Perform Hypothesis Tests4.11 Hypothesis Testing for the Proportion; THE R ZONE; Reference; Exercises; 5 Multivariate Statistics; 5.1 Two-Sample t-Test for Difference in Means; 5.2 Two-Sample Z-Test for Difference in Proportions; 5.3 Test for Homogeneity of Proportions; 5.4 Chi-Square Test for Goodness of Fit of Multinomial Data; 5.5 Analysis of Variance; 5.6 Regression Analysis; 5.7 Hypothesis Testing in Regression; 5.8 Measuring the Quality of a Regression Model; 5.9 Dangers of Extrapolation; 5.10 Confidence Intervals for the Mean Value of Given 5.11 Prediction Intervals for a Randomly Chosen Value of Given |
| Record Nr. | UNINA-9910817415203321 |
|
Larose Daniel T.
|
||
| Hoboken, New Jersey : , : IEEE, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||