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by Carlo Cattani, Yeliz Karaca
Computational Methods for Data Analysis
Cover
Title Page
Copyright
Preface
Acknowledgment
Contents
1 Introduction
1.1 Objectives
1.2 Intended audience
1.3 Features of the chapters
1.4 Content of the chapters
1.5 Text supplements
1.5.1 Datasets
2 Dataset
2.1 Big data and data science
2.2 Data objects, attributes and types of attributes
2.2.1 Nominal attributes and numeric attributes
2.3 Basic definitions of real and synthetic data
2.3.1 Real dataset
2.3.2 Synthetic dataset
2.4 Real and synthetic data from the fields of economy and medicine
2.4.1 Economy data: Economy (U.N.I.S.) dataset
2.4.2 MS data and content (neurology and radiology data): MS dataset
2.4.3 Clinical psychology data: WAIS-R dataset
2.5 Basic statistical descriptions of data
2.5.1 Central tendency: Mean, median and mode
2.5.2 Spread of data
2.5.3 Measures of data dispersion
2.5.4 Graphic displays
2.6 Data matrix versus dissimilarity matrix
References
3 Data preprocessing and model evaluation
3.1 Data quality
3.2 Data preprocessing: Major steps involved
3.2.1 Data cleaning and methods
3.2.2 Data cleaning as a process
3.2.3 Data integration and methods
3.3 Data value conflict detection and resolution
3.4 Data smoothing and methods
3.5 Data reduction
3.6 Data transformation
3.7 Attribute subset selection
3.8 Classification of data
3.8.1 Definition of classification
3.9 Model evaluation and selection
3.9.1 Metrics for evaluating classifier performance
References
4 Algorithms
4.1 What is an algorithm?
4.1.1 What is the flowchart of an algorithm?
4.1.2 Fundamental concepts of programming
4.1.3 Expressions and repetition statements
4.2 Image on coordinate systems
4.2.1 Pixel coordinates
4.2.2 Color models
4.3 Statistical application with algorithms: Image on coordinate systems
4.3.1 Statistical application with algorithms: BW image on coordinate systems
4.3.2 Statistical application with algorithms: RGB image on coordinate systems
References
5 Linear model and multilinear model
5.1 Linear model analysis for various data
5.1.1 Application of economy dataset based on linear model
5.1.2 Linear model for the analysis of MS
5.1.3 Linear model for the analysis of mental functions
5.2 Multilinear model algorithms for the analysis of various data
5.2.1 Multilinear model for the analysis of economy (U.N.I.S.) dataset
5.2.2 Multilinear model for the analysis of MS
5.2.3 Multilinear model for the analysis of mental functions
References
6 Decision Tree
6.1 Decision tree induction
6.2 Attribute selection measures
6.3 Iterative dichotomiser 3 (ID3) algorithm
6.3.1 ID3 algorithm for the analysis of various data
6.4 C4.5 algorithm
6.4.1 C4.5 Algorithm for the analysis of various data
6.5 CART algorithm
6.5.1 CART algorithm for the analysis of various data
References
7 Naive Bayesian classifier
7.1 Naive Bayesian classifier algorithm (and its types) for the analysis of various data
7.1.1 Naive Bayesian classifier algorithm for the analysis of economy (U.N.I.S.)
7.1.2 Algorithms for the analysis of multiple sclerosis
7.1.3 Naive Bayesian classifier algorithm for the analysis of mental functions
References
8 Support vector machines algorithms
8.1 The case with data being linearly separable
8.2 The case when the data are linearly inseparable
8.3 SVM algorithm for the analysis of various data
8.3.1 SVM algorithm for the analysis of economy (U.N.I.S.) Data
8.3.2 SVM algorithm for the analysis of multiple sclerosis
8.3.3 SVM algorithm for the analysis of mental functions
References
9 k-Nearest neighbor algorithm
9.1 k-Nearest algorithm for the analysis of various data
9.1.1 k-Nearest algorithm for the analysis of Economy (U.N.I.S.)
9.1.2 k-Nearest algorithm for the analysis of multiple sclerosis
9.1.3 k-Nearest algorithm for the analysis of mental functions
References
10 Artificial neural networks algorithm
10.1 Classification by backpropagation
10.1.1 A multilayer feed-forward neural network
10.2 Feed-forward backpropagation (FFBP) algorithm
10.2.1 FFBP algorithm for the analysis of various data
10.3 LVQ algorithm
10.3.1 LVQ algorithm for the analysis of various data
References
11 Fractal and multifractal methods with ANN
11.1 Basic descriptions of fractal
11.2 Fractal dimension
11.3 Multifractal methods
11.3.1 Two-dimensional fractional Brownian motion
11.3.2 Hölder regularity
11.3.3 Fractional Brownian motion
11.4 Multifractal analysis with LVQ algorithm
11.4.1 Polynomial Hölder function with LVQ algorithm for the analysis of various data
11.4.2 Exponential Hölder function with LVQ algorithm for the analysis of various data
References
Index
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Yeliz Karaca, Carlo Cattani
Computational Methods for Data Analysis
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