Data Mining: Concepts, Models and Techniques (Intelligent Systems Reference Library, 12)

✍ Scribed by Florin Gorunescu

• Publisher: Springer
• Year: 2011
• Tongue: English
• Leaves: 364
• Category: Library

✦ Synopsis

The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the ‘natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since “knowledge is power”. The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be applied in real-world situations. Accordingly, it is meant for all those who wish to learn how to explore and analysis of large quantities of data in order to discover the hidden nugget of information.

✦ Table of Contents

Title
Preface
Contents
Introduction to Data Mining
What Is and What Is Not Data Mining?
Why Data Mining?
How to Mine the Data?
Problems Solvable with Data Mining
Classification
Cluster Analysis
Association Rule Discovery
Sequential Pattern Discovery
Regression
Deviation/Anomaly Detection
About Modeling and Models
Data Mining Applications
Data Mining Terminology
Privacy Issues
The “Data-Mine”
What Are Data?
Types of Datasets
Data Quality
Types of Attributes
Exploratory Data Analysis
What Is Exploratory Data Analysis?
Descriptive Statistics
Descriptive Statistics Parameters
Descriptive Statistics of a Couple of Series
Graphical Representation of a Dataset
Analysis of Correlation Matrix
Data Visualization
Examination of Distributions
Advanced Linear and Additive Models
Multiple Linear Regression
Logistic Regression
Cox Regression Model
Additive Models
Time Series: Forecasting
Multivariate Exploratory Techniques
Factor Analysis
Principal Components Analysis
Canonical Analysis
Discriminant Analysis
OLAP
Anomaly Detection
Classification and Decision Trees
What Is a Decision Tree?
Decision Tree Induction
GINI Index
Entropy
Misclassification Measure
Practical Issues Regarding Decision Trees
Predictive Accuracy
STOP Condition for Split
Pruning Decision Trees
Extracting Classification Rules from Decision Trees
Advantages of Decision Trees
Data Mining Techniques and Models
Data Mining Methods
Bayesian Classifier
Artificial Neural Networks
Perceptron
Types of Artificial Neural Networks
Probabilistic Neural Networks
Some Neural Networks Applications
Support Vector Machines
Association Rule Mining
Rule-Based Classification
k-Nearest Neighbor
Rough Sets
Clustering
Hierarchical Clustering
Non-hierarchical/Partitional Clustering
Genetic Algorithms
Components of GAs
Architecture of GAs
Applications
Classification Performance Evaluation
Costs and Classification Accuracy
ROC (Receiver Operating Characteristic) Curve
Statistical Methods for Comparing Classifiers
References
Index