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 (Central Queensland University, Australia) and Yang Xiang (Central Queensland University, Australia)
Indexed In: SCOPUS
Release Date: November, 2009|Copyright: © 2010 |Pages: 516
ISBN13: 9781605669083|ISBN10: 1605669083|EISBN13: 9781605669090|DOI: 10.4018/978-1-60566-908-3

Description

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.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Bayesian networks
  • Cointegrated econometric models
  • Concept-based mining model
  • Data Mining Techniques
  • Financial data mining
  • Intrusion Detection
  • Outlier detection-based alert reduction model
  • Pattern discovery from biological data
  • Process analysis on small databases
  • Spread of activation methods
  • Time series analysis
  • Web personalization

Reviews and Testimonials

This book discusses advances in data mining research in today's dynamic and rapid growing global economical and technological environments. This book provides readers the current state of knowledge, research results, and innovations in data mining, from different aspects such as techniques, algorithms, and applications, and introduces current development in this area.

– A B M Shawkat Ali, Central Queensland University, Australia

Table of Contents and List of Contributors

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Preface

World database is increasing very rapidly due to the uses of advanced computer technology. Data is available now everywhere, for instance, in businesses, science, medical, engineering and so on. Now a challenging question is how we can make these data be the useful elements. The solution is data mining.  Data Mining is a comparatively new research area. But within short time, it has already established the discipline capability in many domains. This new technology is facing many challenges to solve users’ real problems.

The objective of this book is to discuss advances in data mining research in today’s dynamic and rapid growing global economical and technological environments. This book aims to provide readers the current state of knowledge, research results, and innovations in data mining, from different aspects such as techniques, algorithms, and applications. It introduces current development in this area by a systematic approach. The book will serve as an important reference tool for researchers and practitioners in data mining research, a handbook for upper level undergraduate students and postgraduate research students, and a repository for technologists. The value and main contribution of the book lies in the joint exploration of diverse issues towards design, implementation, analysis, evaluation of data mining solutions to the challenging problems in all areas of information technology and science.

Nowadays many data mining books focus on data mining technologies or narrow specific areas. The motivation for this book is to provide readers with the update that covers the current development of the methodology, techniques and applications. In this point, this book will be a special contribution to the data mining research area.

We believe the book to be a unique publication that systematically presents a cohesive view of all the important aspects of modern data mining. The scholarly value of this book and its contributions to the literature in the information technology discipline are that:

•    This book increases the understanding of modern data mining methodology and techniques.
•    This book identifies the recent key challenges which are faced by data mining users.
•    This book is helpful for first time data mining users, since methodology, techniques and application all are under in the a single cover.
•    This book describes the most recent applications on data mining techniques.

The unique structures of our book include: literature review, focus the limitations of the existing techniques, possible solutions, and future trends of the data mining discipline. Data Mining new users and new researchers will be able to find help from this book easily.

The book is suitable to any one who needs an informative introduction to the current development, basic methodology and advanced techniques of data mining. It serves as a handbook for researchers, practitioners, and technologists. It can also be used as textbook for one-semester course for senior undergraduates and postgraduates. It facilitates discussion and idea sharing. It helps researchers exchange their views on experimental design and the future challenges on such discovery techniques. This book will also be helpful to those who are from outside of computer science discipline to understand data mining methodology.

This book is a web of interconnected and substantial materials about data mining methodology, techniques, and applications. The outline of the book is given below.

Chapter I. Data Mining Techniques for Web Personalization: Algorithms and Applications.

Chapter II. Patterns Relevant to the Temporal Data-Context of an Alarm of Interest.

Chapter III. ODARM: an Outlier Detection-Based Alert Reduction Model.

Chapter IV. Concept-based Mining Model.

Chapter V. Intrusion Detection Using Machine Learning: Past and Present.

Chapter VI. A Re-ranking Method of Search Results Based on Keyword and User Interest.

Chapter VII. On The Mining of Cointegrated Econometric Models.

Chapter VIII. Spread of Activation Methods.

Chapter IX. Pattern Discovery from Biological Data.

Chapter X. Introduction to Clustering: Algorithms and Applications.

Chapter XI. Financial Data Mining using Flexible ICA-GARCH Models.

Chapter XII. Machine Learning Techniques for Network Intrusion Detection.

Chapter XIII. Fuzzy Clustering Based Image Segmentation Algorithms.

Chapter XIV. Bayesian Networks in the Health Domain.

Chapter XV. Time Series Analysis.

Chapter XVI. Application of Machine Learning techniques for Railway Health Monitoring.

Chapter XVII. Use of Data Mining Techniques for Process Analysis on Small Databases.

Despite the fact that many researchers contributed to the text, this book is much more than an edited collection of chapters written by separate authors. It systematically presents a cohesive view of all the important aspects of modern data mining.

We are grateful to the researchers who contributed the chapters. We would like to acknowledge research grants we received, in particular, the Central Queensland University Research Advancement Award Scheme RAAS ECF 0804 and the Central Queensland University Research Development and Incentives Program RDI S 0805. We also would like to express our appreciations to the editors in IGI Global, especially Joel A. Gamon, for their excellent professional support.

Finally we are grateful to the family of each of us for their consistent and persistent supports. Shawkat would like to present the book to Jesmin, Nabila, Proma and Shadia. Yang would like to present the book to Abby, David and Julia.

Author(s)/Editor(s) Biography

A B M Shawkat Ali has been involved with research and teaching in Australia and overseas universities at undergraduate and postgraduate levels over the past 12 years. He holds a PhD in Information Technology from Monash University (Australia) in statistical learning theory. He is an author of a data mining textbook published by Thomson and has published over 55 book chapters, journals, and conference papers in the fields of data mining, bioinformatics, telecommunications, and sensor networking. He has served as the Program Chair for the IEEE International Workshop on Data Mining and Artificial Intelligence since 2008. He has also been a PC member for many international conferences including IEEE AINA, IEEE/ACIS ICCIS and IEEE ICAINA.
Yang Xiang is currently with the School of Management and Information Systems, Central Queensland University (Australia). His research interests include network security and distributed systems. In particular, he is currently working in a research group developing active defense systems against large-scale network attacks and new Internet security countermeasures. He has served as the PC Chair for the 11th IEEE International Conference on High Performance Computing and Communications (HPCC 2009), the 3rd International Conference on Network and System Security (NSS 2009), and the 14th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2008). He has been PC member for many international conferences such as IEEE ICC, IEEE GLOBECOM and IEEE ICPADS. He is on the editorial board of the Journal of Network and Computer Applications.

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