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More About This Textbook
Overview
Focusing on three applications of data mining, Design and Implementation of Data Mining Tools explains how to create and employ systems and tools for intrusion detection, Web page surfing prediction, and image classification. Mainly based on the authors’ own research work, the book takes a practical approach to the subject.
The first part of the book reviews data mining techniques, such as artificial neural networks and support vector machines, as well as data mining applications. The second section covers the design and implementation of data mining tools for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The third part presents techniques to solve the WWW prediction problem. The final part describes models that the authors have developed for image classification.
Showing step by step how data mining tools are developed, this hands-on guide discusses the performance results, limitations, and unique contributions of data mining systems. It provides essential information for technologists to decide on the tools to select for a particular application, for developers to focus on alternative designs if an approach is unsuitable, and for managers to choose whether to proceed with a data mining project.
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Table of Contents
DATA MINING TECHNIQUES AND APPLICATIONS
Introduction
Trends
Data Mining Techniques and Applications
Data Mining for Cyber Security: Intrusion Detection
Data Mining for Web: Web Page Surfing Prediction
Data Mining for Multimedia: Image Classification
Organization of This Book
Next Steps
Data Mining Techniques
Introduction
Overview of Data Mining Tasks and Techniques
Artificial Neural Networks
Support Vector Machines
Markov Model
Association Rule Mining (ARM)
Multiclass Problem
Image Mining
Summary
Data Mining Applications
Introduction
Intrusion Detection
Web Page Surfing Prediction
Image Classification
Summary
DATA MINING TOOL FOR INTRUSION DETECTION
Data Mining for Security Applications
Overview
Data Mining for Cyber Security
Current Research and Development
Summary and Directions
Dynamic Growing Self-Organizing Tree Algorithm
Overview
Our Approach
DGSOT
Discussion
Summary and Directions
Intrusion Detection Results
Overview
Dataset
Results
Complexity Validation
Discussion
Summary and Directions
DATA MINING TOOL FOR WEB PAGE SURFING PREDICTION
Web Data Management and Mining
Overview
Digital Libraries
E-Commerce Technologies
Semantic Web Technologies
Web Data Mining
Summary and Directions
Effective Web Page Prediction Using Hybrid Model
Overview
Our Approach
Feature Extraction
Domain Knowledge and Classifier Reduction
Summary
Multiple Evidence Combination for WWW Prediction
Overview
Fitting a Sigmoid after SVM
Fitting a Sigmoid after ANN Output
Dempster–Shafer for Evidence Combination
Dempster’s Rule for Evidence Combination
Using Dempster–Shafer Theory in WWW Prediction
Summary and Directions
WWW Prediction Results
Overview
Terminology
Data Processing
Experiment Setup
Results
Discussion
Summary and Directions
DATA MINING TOOL FOR IMAGE CLASSIFICATION
Multimedia Data Management and Mining
Overview
Managing and Mining Multimedia Data
Management and Mining Text, Image, Audio, and Video Data
Summary and Directions
Image Classification Models
Overview
Example Models
Image Classification
Summary
Subspace Clustering and Automatic Image Annotation
Introduction
Proposed Automatic Image Annotation Framework
The Vector Space Model
Clustering Algorithm for Blob Token Generation
Construction of the Probability Table
AutoAnnotation
Experimental Setup
Evaluation Methods
Results
Summary
Enhanced Weighted Feature Selection
Introduction
Aggressive Feature Weighting Algorithm
Experiment Results
Summary and Directions
Image Classification and Performance Analysis
Introduction
Classifiers
Evidence Theory and KNN
Experiment Results
Discussion
Summary and Directions
Summary and Directions
Overview
Summary of This Book
Directions for Data Mining Tools
Where Do We Go from Here
Appendix: Data Management Systems: Developments and Trends
Overview
Developments in Database Systems
Status, Vision, and Issues
Data Management Systems Framework
Building Information Systems from the Framework
Relationship between the Texts
Summary and Directions
Index
References appear at the end of each chapter.