## Book Description

This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.

1. Cover
2. Title Page
4. Preface
5. Acknowledgment
6. Contents
7. 1 Introduction
1. 1.1 Objectives
2. 1.2 Intended audience
3. 1.3 Features of the chapters
4. 1.4 Content of the chapters
5. 1.5 Text supplements
8. 2 Dataset
1. 2.1 Big data and data science
2. 2.2 Data objects, attributes and types of attributes
3. 2.3 Basic definitions of real and synthetic data
4. 2.4 Real and synthetic data from the fields of economy and medicine
5. 2.5 Basic statistical descriptions of data
6. 2.6 Data matrix versus dissimilarity matrix
7. References
9. 3 Data preprocessing and model evaluation
1. 3.1 Data quality
2. 3.2 Data preprocessing: Major steps involved
3. 3.3 Data value conflict detection and resolution
4. 3.4 Data smoothing and methods
5. 3.5 Data reduction
6. 3.6 Data transformation
7. 3.7 Attribute subset selection
8. 3.8 Classification of data
9. 3.9 Model evaluation and selection
10. References
10. 4 Algorithms
1. 4.1 What is an algorithm?
2. 4.2 Image on coordinate systems
3. 4.3 Statistical application with algorithms: Image on coordinate systems
4. References
11. 5 Linear model and multilinear model
1. 5.1 Linear model analysis for various data
2. 5.2 Multilinear model algorithms for the analysis of various data
3. References
12. 6 Decision Tree
1. 6.1 Decision tree induction
2. 6.2 Attribute selection measures
3. 6.3 Iterative dichotomiser 3 (ID3) algorithm
4. 6.4 C4.5 algorithm
5. 6.5 CART algorithm
6. References
13. 7 Naive Bayesian classifier
1. 7.1 Naive Bayesian classifier algorithm (and its types) for the analysis of various data
2. References
14. 8 Support vector machines algorithms
1. 8.1 The case with data being linearly separable
2. 8.2 The case when the data are linearly inseparable
3. 8.3 SVM algorithm for the analysis of various data
4. References
15. 9 k-Nearest neighbor algorithm
1. 9.1 k-Nearest algorithm for the analysis of various data
2. References
16. 10 Artificial neural networks algorithm
1. 10.1 Classification by backpropagation
2. 10.2 Feed-forward backpropagation (FFBP) algorithm
3. 10.3 LVQ algorithm
4. References
17. 11 Fractal and multifractal methods with ANN
1. 11.1 Basic descriptions of fractal
2. 11.2 Fractal dimension
3. 11.3 Multifractal methods
4. 11.4 Multifractal analysis with LVQ algorithm
5. References
18. Index

## Product Information

• Title: Computational Methods for Data Analysis
• Author(s): Yeliz Karaca, Carlo Cattani
• Release date: December 2018
• Publisher(s): De Gruyter
• ISBN: 9783110493603