Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches

Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches

A B M Shawkat Ali, Yang Xiang

Copyright: © 2010 |Pages: 516

ISBN13: 9781605669083|ISBN10: 1605669083|EISBN13: 9781605669090|ISBN13 Softcover: 9781616924461

DOI: 10.4018/978-1-60566-908-3

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MLA

Ali, A B M Shawkat, and Yang Xiang, editors. Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches. IGI Global, 2010. https://doi.org/10.4018/978-1-60566-908-3

APA

Ali, A. S. & Xiang, Y. (Eds.). (2010). Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches. IGI Global. https://doi.org/10.4018/978-1-60566-908-3

Chicago

Ali, A B M Shawkat, and Yang Xiang, eds. Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-908-3

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Overcoming many challenges, data mining has already established discipline capability in many domains.

Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches discusses advances in modern data mining research in today's rapidly growing global and technological environment. A critical mass of the most sought after knowledge, this publication serves as an important reference tool to leading research within information search and retrieval techniques.

Table of Contents

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Front Materials

Title Page

Copyright Page

Preface

Chapters

Chapter 1

Data Mining Techniques for Web Personalization: Algorithms and Applications (pages 1-17)

Gulden Uchyigit

The increase in the information overload problem poses new challenges in the area of web personalization. Traditionally, data mining techniques have been extensively employed in the area of personalization, in particular data...

Chapter 2

Patterns Relevant to the Temporal Data-Context of an Alarm of Interest (pages 18-39)

Savo Kordic, Chiou Peng Lam, Jitian Xiao, Huaizhong Li

The productivity of chemical plants and petroleum refineries depends on the performance of alarm systems. Alarm history collected from distributed control systems (DCS) provides useful information about past plant alarm system...

Chapter 3

ODARM: An Outlier Detection-Based Alert Reduction Model (pages 40-56)

Fu Xiao, Xie Li

Intrusion Detection Systems (IDSs) are widely deployed with increasing of unauthorized activities and attacks. However they often overload security managers by triggering thousands of alerts per day. And up to 99% of these alerts are...

Chapter 4

Concept-Based Mining Model (pages 57-69)

Shady Shehata, Fakhri Karray, Mohamed Kamel

Most of text mining techniques are based on word and/or phrase analysis of the text. Statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency...

Chapter 5

Intrusion Detection Using Machine Learning: Past and Present (pages 70-107)

Mohammed M. Mazid, A. B.M. Shawkat Ali, Kevin S. Tickle

Intrusion detection has received enormous attention from the beginning of computer network technology. It is the task of detecting attacks against a network and its resources. To detect and counteract any unauthorized activity, it is...

Chapter 6

A Re-Ranking Method of Search Results Based on Keyword and User Interest (pages 108-121)

Ming Xu, Hong-Rong Yang, Ning Zheng

It is a pivotal task for a forensic investigator to search a hard disk to find interesting evidences. Currently, most search tools in digital forensic field, which utilize text string match and index technology, produce high recall...

Chapter 7

On the Mining of Cointegrated Econometric Models (pages 122-135)

J.L. van Velsen, R. Choenni

The authors describe a process of extracting a cointegrated model from a database. An important part of the process is a model generator that automatically searches for cointegrated models and orders them according to an information...

Chapter 8

Spreading Activation Methods (pages 136-167)

Alexander Troussov, Eugene Levner, Cristian Bogdan, John Judge, Dmitri Botvich

Spreading activation (also known as spread of activation) is a method for searching associative networks, neural networks or semantic networks. The method is based on the idea of quickly spreading an associative relevancy measure...

Chapter 9

Pattern Discovery from Biological Data (pages 168-223)

Jesmin Nahar, Kevin S. Tickle, A. B.M. Shawkat Ali

Extracting useful information from structured and unstructured biological data is crucial in the health industry. Some examples include medical practitioner’s need to identify breast cancer patient in the early stage, estimate...

Chapter 10

Introduction to Clustering: Algorithms and Applications (pages 224-254)

Raymond Greenlaw, Sanpawat Kantabutra

This chapter provides the reader with an introduction to clustering algorithms and applications. A number of important well-known clustering methods are surveyed. The authors present a brief history of the development of the field of...

Chapter 11

Financial Data Mining Using Flexible ICA-GARCH Models (pages 255-272)

Philip L.H. Yu, Edmond H.C. Wu, W.K. Li

As a data mining technique, independent component analysis (ICA) is used to separate mixed data signals into statistically independent sources. In this chapter, we apply ICA for modeling multivariate volatility of financial asset...

Chapter 12

Machine Learning Techniques for Network Intrusion Detection (pages 273-299)

Tich Phuoc Tran, Pohsiang Tsai, Tony Jan, Xiangjian He

Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of cyber attacks on distributed computer systems. Therefore, an automated and adaptive defensive tool...

Chapter 13

Fuzzy Clustering Based Image Segmentation Algorithms (pages 300-341)

M. Ameer Ali

Image segmentation especially fuzzy based image segmentation techniques are widely used due to effective segmentation performance. For this reason, a huge number of algorithms are proposed in the literature. This chapter presents a...

Chapter 14

Bayesian Networks in the Health Domain (pages 342-376)

Shyamala G. Nadathur

Large datasets are regularly collected in biomedicine and healthcare (here referred to as the ‘health domain’). These datasets have some unique characteristics and problems. Therefore there is a need for methods which allow modelling...

Chapter 15

Time Series Analysis and Structural Change Detection (pages 377-395)

Kwok Pan Pang

Most research on time series analysis and forecasting is normally based on the assumption of no structural change, which implies that the mean and the variance of the parameter in the time series model are constant over time....

Chapter 16

Application of Machine Learning Techniques for Railway Health Monitoring (pages 396-421)

G.M. Shafiullah, Adam Thompson, Peter J. Wolfs, A.B.M. Shawkat Ali

Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems that ensure secure and reliable operation of the rail vehicle....

Chapter 17

Use of Data Mining Techniques for Process Analysis on Small Databases (pages 422-436)

Matjaz Gams, Matej Ozek

The pharmaceutical industry was for a long time founded on rigid rules. With the new PAT initiative, control is becoming significantly more flexible. The Food and Drug Administration is even encouraging the industry to use methods...

Back Materials

Compilation of References

About the Contributors

Index

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