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Data mining and statistics for decision making / / Stéphane Tufféry; translated by Rod Riesco
| Data mining and statistics for decision making / / Stéphane Tufféry; translated by Rod Riesco |
| Autore | Tuffery Stéphane |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex ; ; Hoboken, NJ., : Wiley, 2011 |
| Descrizione fisica | 1 online resource (717 p.) |
| Disciplina | 006.3/12 |
| Collana | Wiley series in computational statistics |
| Soggetto topico |
Data mining
Statistical decision |
| ISBN |
1-283-37397-1
9786613373977 0-470-97928-3 0-470-97916-X 0-470-97917-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Data Mining and Statistics for Decision Making; Contents; Preface; Foreword; Foreword from the French language edition; List of trademarks; 1 Overview of data mining; 1.1 What is data mining?; 1.2 What is data mining used for?; 1.2.1 Data mining in different sectors; 1.2.2 Data mining in different applications; 1.3 Data mining and statistics; 1.4 Data mining and information technology; 1.5 Data mining and protection of personal data; 1.6 Implementation of data mining; 2 The development of a data mining study; 2.1 Defining the aims; 2.2 Listing the existing data; 2.3 Collecting the data
2.4 Exploring and preparing the data2.5 Population segmentation; 2.6 Drawing up and validating predictive models; 2.7 Synthesizing predictive models of different segments; 2.8 Iteration of the preceding steps; 2.9 Deploying the models; 2.10 Training the model users; 2.11 Monitoring the models; 2.12 Enriching the models; 2.13 Remarks; 2.14 Life cycle of a model; 2.15 Costs of a pilot project; 3 Data exploration and preparation; 3.1 The different types of data; 3.2 Examining the distribution of variables; 3.3 Detection of rare or missing values; 3.4 Detection of aberrant values 3.5 Detection of extreme values3.6 Tests of normality; 3.7 Homoscedasticity and heteroscedasticity; 3.8 Detection of the most discriminating variables; 3.8.1 Qualitative, discrete or binned independent variables; 3.8.2 Continuous independent variables; 3.8.3 Details of single-factor non-parametric tests; 3.8.4 ODS and automated selection of discriminating variables; 3.9 Transformation of variables; 3.10 Choosing ranges of values of binned variables; 3.11 Creating new variables; 3.12 Detecting interactions; 3.13 Automatic variable selection; 3.14 Detection of collinearity; 3.15 Sampling 3.15.1 Using sampling3.15.2 Random sampling methods; 4 Using commercial data; 4.1 Data used in commercial applications; 4.1.1 Data on transactions and RFM Data; 4.1.2 Data on products and contracts; 4.1.3 Lifetimes; 4.1.4 Data on channels; 4.1.5 Relational, attitudinal and psychographic data; 4.1.6 Sociodemographic data; 4.1.7 When data are unavailable; 4.1.8 Technical data; 4.2 Special data; 4.2.1 Geodemographic data; 4.2.2 Profitability; 4.3 Data used by business sector; 4.3.1 Data used in banking; 4.3.2 Data used in insurance; 4.3.3 Data used in telephony; 4.3.4 Data used in mail order 5 Statistical and data mining software5.1 Types of data mining and statistical software; 5.2 Essential characteristics of the software; 5.2.1 Points of comparison; 5.2.2 Methods implemented; 5.2.3 Data preparation functions; 5.2.4 Other functions; 5.2.5 Technical characteristics; 5.3 The main software packages; 5.3.1 Overview; 5.3.2 IBM SPSS; 5.3.3 SAS; 5.3.4 R; 5.3.5 Some elements of the R language; 5.4 Comparison of R, SAS and IBM SPSS; 5.5 How to reduce processing time; 6 An outline of data mining methods; 6.1 Classification of the methods; 6.2 Comparison of the methods; 7 Factor analysis 7.1 Principal component analysis |
| Record Nr. | UNINA-9910130875003321 |
Tuffery Stéphane
|
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| Chichester, West Sussex ; ; Hoboken, NJ., : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep learning : from big data to artificial intelligence with R / / Stéphane Tufféry
| Deep learning : from big data to artificial intelligence with R / / Stéphane Tufféry |
| Autore | Tuffery Stéphane |
| Pubbl/distr/stampa | Chichester, West Sussex : , : Wiley, , 2023 |
| Descrizione fisica | 1 online resource (542 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Deep learning (Machine learning)
Big data - Statistical methods R (Computer program language) |
| ISBN |
1-119-84504-1
1-119-84502-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Acknowledgements -- Introduction -- Chapter 1 From Big Data to Deep Learning -- 1.1 Introduction -- 1.2 Examples of the Use of Big Data and Deep Learning -- 1.3 Big Data and Deep Learning for Companies and Organizations -- 1.3.1 Big Data in Finance -- 1.3.1.1 Google Trends -- 1.3.1.2 Google Trends and Stock Prices -- 1.3.1.3 The quantmod Package for Financial Analysis -- 1.3.1.4 Google Trends in R -- 1.3.1.5 Matching Data from quantmod and Google Trends -- 1.3.2 Big Data and Deep Learning in Insurance -- 1.3.3 Big Data and Deep Learning in Industry -- 1.3.4 Big Data and Deep Learning in Scientific Research and Education -- 1.3.4.1 Big Data in Physics and Astrophysics -- 1.3.4.2 Big Data in Climatology and Earth Sciences -- 1.3.4.3 Big Data in Education -- 1.4 Big Data and Deep Learning for Individuals -- 1.4.1 Big Data and Deep Learning in Healthcare -- 1.4.1.1 Connected Health and Telemedicine -- 1.4.1.2 Geolocation and Health -- 1.4.1.3 The Google Flu Trends -- 1.4.1.4 Research in Health and Medicine -- 1.4.2 Big Data and Deep Learning for Drivers -- 1.4.3 Big Data and Deep Learning for Citizens -- 1.4.4 Big Data and Deep Learning in the Police -- 1.5 Risks in Data Processing -- 1.5.1 Insufficient Quantity of Training Data -- 1.5.2 Poor Data Quality -- 1.5.3 Non‐Representative Samples -- 1.5.4 Missing Values in the Data -- 1.5.5 Spurious Correlations -- 1.5.6 Overfitting -- 1.5.7 Lack of Explainability of Models -- 1.6 Protection of Personal Data -- 1.6.1 The Need for Data Protection -- 1.6.2 Data Anonymization -- 1.6.3 The General Data Protection Regulation -- 1.7 Open Data -- Notes -- Chapter 2 Processing of Large Volumes of Data -- 2.1 Issues -- 2.2 The Search for a Parsimonious Model -- 2.3 Algorithmic Complexity -- 2.4 Parallel Computing -- 2.5 Distributed Computing -- 2.5.1 MapReduce.
2.5.2 Hadoop -- 2.5.3 Computing Tools for Distributed Computing -- 2.5.4 Column‐Oriented Databases -- 2.5.5 Distributed Architecture and "Analytics" -- 2.5.6 Spark -- 2.6 Computer Resources -- 2.6.1 Minimum Resources -- 2.6.2 Graphics Processing Units (GPU) and Tensor Processing Units (TPU) -- 2.6.3 Solutions in the Cloud -- 2.7 R and Python Software -- 2.8 Quantum Computing -- Notes -- Chapter 3 Reminders of Machine Learning -- 3.1 General -- 3.2 The Optimization Algorithms -- 3.3 Complexity Reduction and Penalized Regression -- 3.4 Ensemble Methods -- 3.4.1 Bagging -- 3.4.2 Random Forests -- 3.4.3 Extra‐Trees -- 3.4.4 Boosting -- 3.4.5 Gradient Boosting Methods -- 3.4.6 Synthesis of the Ensemble Methods -- 3.5 Support Vector Machines -- 3.6 Recommendation Systems -- Notes -- Chapter 4 Natural Language Processing -- 4.1 From Lexical Statistics to Natural Language Processing -- 4.2 Uses of Text Mining and Natural Language Processing -- 4.3 The Operations of Textual Analysis -- 4.3.1 Textual Data Collection -- 4.3.2 Identification of the Language -- 4.3.3 Tokenization -- 4.3.4 Part‐of‐Speech Tagging -- 4.3.5 Named Entity Recognition -- 4.3.6 Coreference Resolution -- 4.3.7 Lemmatization -- 4.3.8 Stemming -- 4.3.9 Simplifications -- 4.3.10 Removal of Stop Words -- 4.4 Vector Representation and Word Embedding -- 4.4.1 Vector Representation -- 4.4.2 Analysis on the Document‐Term Matrix -- 4.4.3 TF‐IDF Weighting -- 4.4.4 Latent Semantic Analysis -- 4.4.5 Latent Dirichlet Allocation -- 4.4.6 Word Frequency Analysis -- 4.4.7 Word2Vec Embedding -- 4.4.8 GloVe Embedding -- 4.4.9 FastText Embedding -- 4.5 Sentiment Analysis -- Notes -- Chapter 5 Social Network Analysis -- 5.1 Social Networks -- 5.2 Characteristics of Graphs -- 5.3 Characterization of Social Networks -- 5.4 Measures of Influence in a Graph -- 5.5 Graphs with R -- 5.6 Community Detection. 5.6.1 The Modularity of a Graph -- 5.6.2 Community Detection by Divisive Hierarchical Clustering -- 5.6.3 Community Detection by Agglomerative Hierarchical Clustering -- 5.6.4 Other Methods -- 5.6.5 Community Detection with R -- 5.7 Research and Analysis on Social Networks -- 5.8 The Business Model of Social Networks -- 5.9 Digital Advertising -- 5.10 Social Network Analysis with R -- 5.10.1 Collecting Tweets -- 5.10.2 Formatting the Corpus -- 5.10.3 Stemming and Lemmatization -- 5.10.4 Example -- 5.10.5 Clustering of Terms and Documents -- 5.10.6 Opinion Scoring -- 5.10.7 Graph of Terms with Their Connotation -- Notes -- Chapter 6 Handwriting Recognition -- 6.1 Data -- 6.2 Issues -- 6.3 Data Processing -- 6.4 Linear and Quadratic Discriminant Analysis -- 6.5 Multinomial Logistic Regression -- 6.6 Random Forests -- 6.7 Extra‐Trees -- 6.8 Gradient Boosting -- 6.9 Support Vector Machines -- 6.10 Single Hidden Layer Perceptron -- 6.11 H2O Neural Network -- 6.12 Synthesis of "Classical" Methods -- Notes -- Chapter 7 Deep Learning -- 7.1 The Principles of Deep Learning -- 7.2 Overview of Deep Neural Networks -- 7.3 Recall on Neural Networks and Their Training -- 7.4 Difficulties of Gradient Backpropagation -- 7.5 The Structure of a Convolutional Neural Network -- 7.6 The Convolution Mechanism -- 7.7 The Convolution Parameters -- 7.8 Batch Normalization -- 7.9 Pooling -- 7.10 Dilated Convolution -- 7.11 Dropout and DropConnect -- 7.12 The Architecture of a Convolutional Neural Network -- 7.13 Principles of Deep Network Learning for Computer Vision -- 7.14 Adaptive Learning Algorithms -- 7.15 Progress in Image Recognition -- 7.16 Recurrent Neural Networks -- 7.17 Capsule Networks -- 7.18 Autoencoders -- 7.19 Generative Models -- 7.19.1 Generative Adversarial Networks -- 7.19.2 Variational Autoencoders -- 7.20 Other Applications of Deep Learning. 7.20.1 Object Detection -- 7.20.2 Autonomous Vehicles -- 7.20.3 Analysis of Brain Activity -- 7.20.4 Analysis of the Style of a Pictorial Work -- 7.20.5 Go and Chess Games -- 7.20.6 Other Games -- Notes -- Chapter 8 Deep Learning for Computer Vision -- 8.1 Deep Learning Libraries -- 8.2 MXNet -- 8.2.1 General Information about MXNet -- 8.2.2 Creating a Convolutional Network with MXNet -- 8.2.3 Model Management with MXNet -- 8.2.4 CIFAR‐10 Image Recognition with MXNet -- 8.3 Keras and TensorFlow -- 8.3.1 General Information about Keras -- 8.3.2 Application of Keras to the MNIST Database -- 8.3.3 Application of Pre‐Trained Models -- 8.3.4 Explain the Prediction of a Computer Vision Model -- 8.3.5 Application of Keras to CIFAR‐10 Images -- 8.3.6 Classifying Cats and Dogs -- 8.4 Configuring a Machine's GPU for Deep Learning -- 8.4.1 Checking the Compatibility of the Graphics Card -- 8.4.2 NVIDIA Driver Installation -- 8.4.3 Installation of Microsoft Visual Studio -- 8.4.4 NVIDIA CUDA Toolkit Installation -- 8.4.5 Installation of cuDNN -- 8.5 Computing in the Cloud -- 8.6 PyTorch -- 8.6.1 The Python PyTorch Package -- 8.6.2 The R torch Package -- Notes -- Chapter 9 Deep Learning for Natural Language Processing -- 9.1 Neural Network Methods for Text Analysis -- 9.2 Text Generation Using a Recurrent Neural Network LSTM -- 9.3 Text Classification Using a LSTM or GRU Recurrent Neural Network -- 9.4 Text Classification Using a H2O Model -- 9.5 Application of Convolutional Neural Networks -- 9.6 Spam Detection Using a Recurrent Neural Network LSTM -- 9.7 Transformer Models, BERT, and Its Successors -- Notes -- Chapter 10 Artificial Intelligence -- 10.1 The Beginnings of Artificial Intelligence -- 10.2 Human Intelligence and Artificial Intelligence -- 10.3 The Different Forms of Artificial Intelligence. 10.4 Ethical and Societal Issues of Artificial Intelligence -- 10.5 Fears and Hopes of Artificial Intelligence -- 10.6 Some Dates of Artificial Intelligence -- Notes -- Conclusion -- Annotated Bibliography -- On Big Data and High Dimensional Statistics -- On Deep Learning -- On Artificial Intelligence -- On the Use of R and Python in Data Science and on Big Data -- Index -- EULA. |
| Record Nr. | UNINA-9910830365503321 |
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Tuffery Stéphane
|
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| Chichester, West Sussex : , : Wiley, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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