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EbookBell Team
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ISBN 13: 9780470890455
Author: Mehmed Kantardzic
This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.
1. Data-Mining Concepts
1.1 Introduction
1.2 Data-Mining Roots
1.3 Data-Mining Process
1.4 From Data Collection to Data Preprocessing
1.5 Data Warehouses for Data Mining
1.6 From Big Data to Data Science
1.7 Business Aspects of Data Mining: Why a Data-Mining Project Fails?
1.8 Organization of This Book
1.9 Review Questions and Problems
1.10 References for Further Study
2. Preparing the Data
2.1 Representation of Raw Data
2.2 Characteristics of Raw Data
2.3 Transformation of Raw Data
2.4 Missing Data
2.5 Time-Dependent Data
2.6 Outlier Analysis
2.7 Review Questions and Problems
2.8 References for Further Study
3. Data Reduction
3.1 Dimensions of Large Data Sets
3.2 Features Reduction
3.3 Relief Algorithm
3.4 Entropy Measure for Ranking Features
3.5 Principal Component Analysis
3.6 Values Reduction
3.7 Feature Discretization: ChiMerge Technique
3.8 Cases Reduction
3.9 Review Questions and Problems
3.10 References for Further Study
4. Learning from Data
4.1 Learning Machine
4.2 Statistical Learning Theory
4.3 Types of Learning Methods
4.4 Common Learning Tasks
4.5 Model Estimation
4.6 Review Questions and Problems
4.7 References for Further Study
5. Statistical Methods
5.1 Statistical Inference
5.2 Assessing Differences in Data Sets
5.3 Bayesian Inference
5.4 Predictive Regression
5.5 Analysis of Variance
5.6 Logistic Regression
5.7 Log-Linear Models
5.8 Linear Discriminant Analysis
5.9 Review Questions and Problems
5.10 References for Further Study
6. Cluster Analysis
6.1 Clustering Concepts
6.2 Similarity Measures
6.3 Agglomerative Hierarchical Clustering
6.4 Partitional Clustering
6.5 Incremental Clustering
6.6 Review Questions and Problems
6.7 References for Further Study
7. Decision Trees and Decision Rules
7.1 Decision Trees
7.2 C4.5 Algorithm: Generating a Decision Tree
7.3 Unknown Attribute Values
7.4 Pruning Decision Tree
7.5 C4.5 Algorithm: Generating Decision Rules
7.6 Limitations of Decision Trees and Decision Rules
7.7 Associative-Classification Method
7.8 Review Questions and Problems
7.9 References for Further Study
8. Association Rules
8.1 Market-Basket Analysis
8.2 Algorithm Apriori
8.3 From Frequent Itemsets to Association Rules
8.4 Improving the Efficiency of the Apriori Algorithm
8.5 Frequent Pattern-Growth Method
8.6 Multidimensional Association-Rules Mining
8.7 Web Mining
8.8 HITS and LOGSOM Algorithms
8.9 Mining Path-Traversal Patterns
8.10 Text Mining
8.11 Review Questions and Problems
8.12 References for Further Study
9. Artificial Neural Networks
9.1 Model of an Artificial Neuron
9.2 Architectures of Artificial Neural Networks
9.3 Learning Process
9.4 Learning Tasks
9.5 Multilayer Perceptrons
9.6 Competitive Networks and Competitive Learning
9.7 Review Questions and Problems
9.8 References for Further Study
10. Genetic Algorithms
10.1 Fundamentals of Genetic Algorithms
10.2 Optimization Using Genetic Algorithms
10.3 A Simple Illustration of a Genetic Algorithm
10.4 Schemata
10.5 Traveling Salesman Problem
10.6 Machine Learning Using Genetic Algorithms
10.7 Review Questions and Problems
10.8 References for Further Study
11. Fuzzy Sets and Fuzzy Logic
11.1 Fuzzy Sets
11.2 Fuzzy Set Operations
11.3 Extension Principle and Fuzzy Relations
11.4 Fuzzy Logic and Fuzzy Inference Systems
11.5 Multifactorial Evaluation
11.6 Extracting Fuzzy Models from Data
11.7 Review Questions and Problems
11.8 References for Further Study
12. Visualization Methods
12.1 Perception and Visualization
12.2 Scientific Visualization and Information Visualization
12.3 Parallel Coordinates
12.4 Radial Visualization
12.5 Kohonen Self-Organized Maps
12.6 Visualization Systems for Data Mining
12.7 Review Questions and Problems
12.8 References for Further Study
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Tags: Mehmed Kantardzic, Data, Concepts
