Data Mining Concepts Models and Techniques 1st Edition by Florin Gorunescu ISBN 3642197213 9783642197215

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Product details:

ISBN 10: 3642197213

ISBN 13: 9783642197215

Author:  Florin Gorunescu 

Data Mining: Concepts, Models and Techniques 1st Edition: 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.

 

Data Mining: Concepts, Models and Techniques 1st Edition Table of contents:

Part I: 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

Part II: The “Data-Mine”

• What Are Data?
• Types of Datasets
• Data Quality
• Types of Attributes

Part III: 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

Part IV: 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

Part V: 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

Part VI: Classification Performance Evaluation

• Costs and Classification Accuracy
• ROC (Receiver Operating Characteristic) Curve
• Statistical Methods for Comparing Classifiers

 

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