book

Practical Applications of Data Mining

Name: Practical Applications of Data Mining
Author: Sang C. Suh
ISBN: 9781449603021

by Sang C. Suh

January 2011

Intermediate to advanced

420 pages

12h 32m

English

Jones & Bartlett Learning

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Cover
Title Page
Copyright Page
Table of Contents (1/2)
Table of Contents (2/2)
Preface (1/2)
Preface (2/2)
Foreword
Foreword
Chapter 1: Introduction to Data Mining
1.1 Traditional Database Management Systems

1.2 Knowledge Discovery in Databases
1.2.1 Pre-Processing
1.2.2 Data Warehousing
1.2.3 Post-Processing1.3 Data-Mining Methods
1.3.1 Association Rules
1.3.2 Classification Learning
1.3.3 Statistical Data Mining
1.3.4 Rough Sets for Data Mining
1.3.5 Neural Networks for Data Mining
1.3.6 Clustering for Data Mining
1.3.7 Fuzzy Sets for Data Mining
1.4 Integrated Framework for Intelligent Databases
1.5 Practical Applications of Data Mining
1.5.1 Healthcare Services
1.5.2 Banking
1.5.3 Supermarket Applications
1.5.4 Medical Image Classification
1.6 Chapter Summary
Chapter 2: Association Rules
2.1 Introduction2.2 Mining of Association Rules in Market Basket Data
2.2.1 Apriori Algorithm
2.2.2 Apriori-gen( ) Function
2.2.3 Apriori Example
2.2.4 AprioriTid Algorithm
2.3 Attribute-Oriented Rule Generalization
2.3.1 Concept Hierarchies
2.3.2 Basic Strategies for Attribute-Oriented Induction
2.3.3 Basic Attribute-Oriented Induction Algorithm
2.3.4 Generation of Discrimination Rules through Attribute-Oriented Induction
2.4 Association Rules in Hypertext Databases
2.4.1 Formal Model
2.4.2 Algorithms for Generating Composite Association Rules
2.5 Quantitative Association Rules
2.5.1 Mapping of Quantitative Association Rules
2.5.2 Problem Decomposition
2.5.3 Partitioning of Quantitative Attributes
2.6 Mining of Compact Rules
2.6.1 Semantic Association Relationships
2.6.2 Generalization Algorithm
2.6.3 Learning Process
2.6.4 Learning Algorithm
2.7 Mining of Time-Constrained Association Rules
2.7.1 Time-Constrained Association Rules
2.7.2 Properties of Time Constraints
2.7.3 Potential Applications
2.8 Chapter Summary
2.9 Exercises
2.10 Selected Bibliographic Notes
2.11 Chapter Bibliography
Chapter 3: Classification Learning
3.1 Introduction
3.2 Knowledge Representation
3.2.1 Classification Rules3.2.2 Decision Trees
3.3 Separate-and-Conquer Approach
3.3.1 Prism
3.3.2 Induct (1/3)
3.3.2 Induct (2/3)
3.3.2 Induct (3/3)
3.3.3 REP, IREP, RIPPER
3.4 Divide-and-Conquer Approach
3.4.1 ID3 (1/2)
3.4.1 ID3 (2/2)
3.4.2 C4.5 and C5.0 (1/4)
3.4.2 C4.5 and C5.0 (2/4)
3.4.2 C4.5 and C5.0 (3/4)
3.4.2 C4.5 and C5.0 (4/4)
3.5 Partial Decision Tree (1/2)
3.5 Partial Decision Tree (2/2)
3.6 Chapter Summary
3.7 Exercises
3.8 Selected Bibliographic Notes
3.9 Chapter Bibliography
Chapter 4: Statistics for Data Mining
4.1 Introduction
4.2 House Sales Data
4.3 Conditional Probability
4.4 Equality Tests
4.5 Correlation Coefficient
4.6 Contingency Table and the x Test (1/2)
4.6 Contingency Table and the x Test (2/2)
4.7 Linear Regression (1/2)
4.7 Linear Regression (2/2)
4.8 House Sales Database Revisited
4.9 Chapter Summary
4.10 Exercises
4.11 Selected Bibliographic Notes
4.12 Chapter Bibliography
Chapter 5: Rough Sets and Bayes’ Theories
5.1 Introduction
5.2 Bayes’ Theorem
5.3 Rough Sets
5.3.1 Data Analysis and Representation
5.3.2 Reduction of Condition Attributes and Generation of Decision Rules
5.4 Applications Based on Bayes’ and Rough Sets
5.4.1 Customer Tendency Analysis Using Bayes’ Theory5.4.2 Contact Lens Prescription Using Rough Set Theory
5.4.3 Welding Procedure Using Rough-Set Theory (1/2)
5.4.3 Welding Procedure Using Rough-Set Theory (2/2)
5.4.4 Classification of Automobiles Using Both Bayes’ and Rough Set Theory (1/2)
5.4.4 Classification of Automobiles Using Both Bayes’ and Rough Set Theory (2/2)
5.5 Chapter Summary
5.6 Exercises (1/2)
5.6 Exercises (2/2)
5.7 Selected Bibliographic Notes
5.8 Chapter Bibliography
Chapter 6: Neural Networks
6.1 Introduction
6.2 Neural Computing and Databases
6.3 Network Classification
6.3.1 Unsupervised Learning Models
6.3.2 Supervised Learning Models
6.4 Parameters of the Learning Process
6.4.1 Number of Hidden Layers
6.4.2 Number of Hidden Nodes
6.4.3 Early Stopping
6.4.4 Convergence Curve (Back-Propagation Neural Network)
6.5 Network Structures
6.5.1 Neural Net and Traditional Classifiers
6.6 Knowledge Discovery in Databases
6.6.1 Normalization
6.7 Backpropagation Neural Network (BPNN) Model
6.7.1 Network Architecture
6.7.2 Algorithm
6.7.3 Example I
6.7.4 Example II (Retrieval of Data Using the BPNN Model)
6.8 Bidirectional Associative Memory (BAM) Model
6.8.1 Network Architecture
6.8.2 Algorithm
6.8.3 Example with Four Training Vectors
6.9 Learning Vector Quantization (LVQ) Model
6.9.1 Network Architecture
6.9.2 Algorithm
6.9.3 Example
6.10 Probabilistic Neural Network (PNN) Model
6.10.1 Network Architecture
6.10.2 Algorithm
6.10.3 Example
6.10.4 Parameter Adjustment Using a Smoothing Factor
6.11 Chapter Summary
6.12 Exercises (1/2)
6.12 Exercises (2/2)
6.13 Selected Bibliographic Notes
6.14 Chapter Bibliography
Chapter 7: Clustering
7.1 Introduction
7.2 Definition of Clusters and Clustering
7.3 Clustering Procedures
7.4 Clustering Concepts
7.4.1 Choosing Variables
7.4.2 Similarity and Dissimilarity Measurement
7.4.3 Standardization of Variables
7.4.4 Weights and Threshold Values
7.4.5 Association Rules
7.5 Clustering Algorithms
7.5.1 Hierarchical Algorithms (1/3)
7.5.1 Hierarchical Algorithms (2/3)
7.5.1 Hierarchical Algorithms (3/3)
7.5.2 Graph Theory Algorithm with the Single-link Method
7.5.3 Partition Algorithms: K-means Algorithm
7.5.4 Density-Search Algorithms
7.5.5 Association Rule Algorithms (1/4)
7.5.5 Association Rule Algorithms (2/4)
7.5.5 Association Rule Algorithms (3/4)
7.5.5 Association Rule Algorithms (4/4)
7.6 Chapter Summary
7.7 Exercises
7.8 Selected Bibliographic Notes
7.9 Chapter Bibliography
Chapter 8: Fuzzy Information Retrieval
8.1 Introduction
8.2 Fuzzy Set Basics
8.3 Fuzzy Set Applications
8.3.1 Project Management
8.3.2 Data Analysis
8.3.3 Nuanced Information Systems
8.4 Linguistic Variables
8.5 Fuzzy Query Processing (1/3)
8.5 Fuzzy Query Processing (2/3)
8.5 Fuzzy Query Processing (3/3)
8.6 Fuzzy Query Processing Using Fuzzy Tables
8.6.1 Convert Raw Data to Fuzzy Member Functions
8.6.2 Fuzzy Table
8.6.3 Fuzzy Search Engine
8.6.4 Fuzzy Table Construction
8.6.5 Fuzzy Query Processing
8.7 Role of Relational Division for Information Retrieval
8.7.1 Information Retrieval through Relational Division
8.7.2 Information Retrieval through Fuzzy Relational Division
8.8 Alpha-Cut Thresholds
8.9 Chapter Summary
8.10 Exercises (1/2)
8.10 Exercises (2/2)
8.11 Selected Bibliographic Notes
8.12 Chapter Bibliography
Appendix (1/3)
Appendix (2/3)
Appendix (3/3)
Index (1/2)
Index (2/2)

Content preview from Practical Applications of Data Mining

3.2 Knowledge representation 81

3.2 KNOWLEDGE REPRESENTATION

Two major kinds of knowledge representation are used in classiﬁcation

learning: the decision tree and the classiﬁcation rule.

3.2.1 Classiﬁcation Rules 

Classiﬁcation rules are the simplest representation of classiﬁcation knowl-

edge. Each classiﬁcation rule is composed of a condition, which includes one

or more tests, and a conclusion, which associates an instance with the class

to which it belongs. Knowledge rules are expressed in the form “if the attri-

bute X is xxx and the attribute Y is yyy, etc., then the instance Z belongs to

class zzz.” To each class there corresponds a set of relevant attribute values.

After the complete set of rules generated from

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Practical Applications of Data Mining

by Sang C. Suh

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