Data analytics

Preface
Contents
List of Symbols
1 Introduction
	1.1 It\'s All About Data
	1.2 Data Analytics, Data Mining, and Knowledge Discovery
	References
2 Data and Relations
	2.1 The Iris Data Set
	2.2 Data Scales
	2.3 Set and Matrix Representations
	2.4 Relations
	2.5 Dissimilarity Measures
	2.6 Similarity Measures
	2.7 Sequence Relations
	2.8 Sampling and Quantization
	Problems
	References
3 Data Preprocessing
	3.1 Error Types
	3.2 Error Handling
	3.3 Filtering
	3.4 Data Transformation
	3.5 Data Integration
	Problems
	References
4 Data Visualization
	4.1 Diagrams
	4.2 Principal Component Analysis
	4.3 Multidimensional Scaling
	4.4 Sammon Mapping
	4.5 Auto-encoder
	4.6 Histograms
	4.7 Spectral Analysis
	Problems
	References
5 Correlation
	5.1 Linear Correlation
	5.2 Correlation and Causality
	5.3 Chi-Square Test for Independence
	Problems
	References
6 Regression
	6.1 Linear Regression
	6.2 Linear Regression with Nonlinear Substitution
	6.3 Robust Regression
	6.4 Neural Networks
	6.5 Radial Basis Function Networks
	6.6 Cross-Validation
	6.7 Feature Selection
	Problems
	References
7 Forecasting
	7.1 Finite State Machines
	7.2 Recurrent Models
	7.3 Autoregressive Models
	Problems
	References
8 Classification
	8.1 Classification Criteria
	8.2 Naive Bayes Classifier
	8.3 Linear Discriminant Analysis
	8.4 Support Vector Machine
	8.5 Nearest Neighbor Classifier
	8.6 Learning Vector Quantization
	8.7 Decision Trees
	Problems
	References
9 Clustering
	9.1 Cluster Partitions
	9.2 Sequential Clustering
	9.3 Prototype-Based Clustering
	9.4 Fuzzy Clustering
	9.5 Relational Clustering
	9.6 Cluster Tendency Assessment
	9.7 Cluster Validity
	9.8 Self-organizing Map
	Problems
	References
A Brief Review of Some Optimization Methods
	A.1 Optimization with Derivatives
	A.2 Gradient Descent
	A.3 Lagrange Optimization
	References
Solutions
	Problems of Chapter 2
	Problems of Chapter 3
	Problems of Chapter 4
	Problems of Chapter 5
	Problems of Chapter 6
	Problems of Chapter 7
	Problems of Chapter 8
	Problems of Chapter 9
Index