Big Data Mining and Complexity 1st Edition by Brian C Castellani, Rajeev Rajaram ISBN 9781526423818 1526423812

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ISBN 10: 1526423812
ISBN 13: 9781526423818
Author: Brian C Castellani, Rajeev Rajaram

Big Data Mining and Complexity 1st Edition Table of contents:

1 Introduction

The Joys of Travel

Data Mining and Big Data Travel

Part I: Thinking Critically and Complex

Organisation of Part I

Part II: The Tools and Techniques of Data Mining

SAGE Quantitative Research Kit

COMPLEX-IT and the SACS Toolkit

The Airline Industry: A Case Study

Part I Thinking Critically and Complex

2 The Failure of Quantitative Social Science

Quantitative Social Science, Then and Now

The Three Phases of Science

What You Should Have Learned in Statistics Class

So, Why Didn’t You Learn These Things?

Changing the Social Life of Data Analysis

3 What Is Big Data?

Big Data as Information Society

Big Data as Global Network Society

The Socio-Cybernetic Web of Big Data

Big Data Databases

The Failed Promise of Big Data

4 What Is Data Mining?

A Bit of Data Mining History

The Data Mining Process

The ‘Black Box’ of Data Mining

Validity and Reliability

The Limits of Normalised Probability Distributions

Fitting Models to Data

Data Mining’s Various Tasks

5 The Complexity Turn

Mapping the Complexity Turn

Data Mining and Big Data as Complexity Science

Top Ten List About Complexity

Number 1

Number 2

Number 3

Number 4

Number 5

Number 6

Number 7

Number 8

Number 9

Number 10

Part II The Tools and Techniques of Data Mining

6 Case-Based Complexity: A Data Mining Vocabulary

Case-Based Complexity

COMPLEX-IT and the SACS Toolkit

The Ontology of Big Data

The Archaeology of Big Data Ontologies

The Formalisms of Case-Based Complexity

What Is a Case?

Two Definitions of a Vector

Cataloguing and Grouping Case Profiles

Mathematical Distance Between Cases

Cataloguing Case Profiles

Diversity of Case Profiles

The Notion of Time t

Profiles That Vary With Time

Static Clustering in Time

Dynamic Clustering of Trajectories

A Vector Field

The State Space

A Discrete Vector Field of Velocities of Trajectories

Why Do We Need a Vector Field?

7 Classification and Clustering

Top Ten Airlines

Classification Versus Clustering

Classification Schemes

Decision Tree Induction

Nearest Neighbour Classifier

Artificial Neural Networks

Support Vector Machines

Clustering

Hierarchical Clustering Methods

Partitioning Methods

Probability Density-Based Methods

Soft Computing Methods

8 Machine Learning

The Smart Airline Industry

What Is Machine Intelligence?

Machine Intelligence for Big Data

Examples of Data Mining Methods Based on Machine Intelligence

Artificial Neural Networks

Genetic Algorithms and Evolutionary Computation

Swarming Travel Routes

Overview of Swarm Intelligence

9 Predictive Analytics and Data Forecasting

Predictive Analytics and Data Forecasting: A Very Brief History

Overview of Techniques

Bayesian Statistics

Bayesian Parameter Estimation

Bayesian Hypothesis Testing

Decision Trees

Neural Networks

Regression (Linear, Non-Linear and Logistic)

10 Longitudinal Analysis

Time Matters

The Complexities of Data

The Limitations of Method

Choosing the Right Method

Growth Mixture Modelling

Growth Curves

Multiple Group Growth Curve Modelling

Growth Mixture Modelling Explained

Differential Equations and Dynamical Systems Theory

Examples of Global–Temporal Dynamics

Modelling Process

Comparing and Contrasting Methods

11 Geospatial Modelling

The Importance of Geospatial Modelling

How Does Geospatial Modelling Work?

Conceptual Framework

Prerequisites

Spatial Relationships

Geospatial Modelling With Real Data

Part 1: Collecting and Organising Geospatial Data

Part 2: Modelling and Analysing Geospatial Data

12 Complex Network Analysis

What Is a Network?

What Are Some Commonly Used Descriptors of Networks?

What Are Some Properties of Networks?

Some Examples of Widely Used Network Models

Key Methods Used to Study Networks

Matrices and Relational Data

Network Methods: A Basic Overview

Modelling Network Dynamics

13 Textual and Visual Data Mining

Thinking About Textual/Visual Data Mining

A Bit of History

Preprocessing Unstructured Data

The Tools of Textual Data Mining

Sentiment Analysis

Visualising Results

14 Conclusion: Advancing a Complex Digital Social Science

Changing the Social Life of Data Analysis

What Does It Take to Model Complex Systems?

A Few Methodological Caveats

Toolkits Rather Than Tools

Breaking Interdisciplinary Barriers

Cross-Pollination of Complex Modelling Problems

Truly Developing New Methods

Keeping Minor Trends

Divide and Conquer Scales

Modelling Complex Causality

Data Heterogeneity and Dependence

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