Design and Implementation of Data Mining Tools: 1st Edition (Hardback) book cover

Design and Implementation of Data Mining Tools

1st Edition

By Bhavani Thuraisingham, Latifur Khan, Mamoun Awad, Lei Wang

Auerbach Publications

272 pages | 113 B/W Illus.

Purchasing Options:$ = USD
Hardback: 9781420045901
pub: 2009-06-18
SAVE ~$18.00
Currently out of stock
$120.00
$102.00
x
eBook (VitalSource) : 9780429116704
pub: 2009-06-18
from $28.98


FREE Standard Shipping!

Description

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.

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.

Subject Categories

BISAC Subject Codes/Headings:
COM021000
COMPUTERS / Database Management / General
COM021030
COMPUTERS / Database Management / Data Mining
COM051230
COMPUTERS / Software Development & Engineering / General