Data Analytics: Models and Algorithms for Intelligent Data Analysis

✍ Scribed by Thomas A. Runkler

• Publisher: Vieweg + Teubner Verlag
• Year: 2020
• Tongue: English
• Leaves: 167
• Edition: 3
• Category: Library

✦ Synopsis

This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.

✦ Table of Contents

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