Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies

Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies

Leon Shyue-Liang Wang (National University of Kaohsiung, Taiwan) and Tzung-Pei Hong (National University of Kaohsiung, Taiwan)
Release Date: March, 2010|Copyright: © 2010 |Pages: 516
ISBN13: 9781615207572|ISBN10: 1615207570|EISBN13: 9781615207589|DOI: 10.4018/978-1-61520-757-2


As the applications of data mining, the non-trivial extraction of implicit information in a data set, have expanded in recent years, so has the need for techniques that are tolerable to imprecision, uncertainty, and approximation.

Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies is a compendium that addresses this need. It integrates contrasting techniques of conventional hard computing and soft computing to exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low-cost solution. This book provides a reference to researchers, practitioners, and students in both soft computing and data mining communities, forming a foundation for the development of the field.

Topics Covered

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

  • Artificial Clonal Selection
  • Biomedical data mining
  • Cat Swarm Optimization
  • Computational Intelligence
  • Data Mining
  • Fuzzy Functional Dependencies
  • Fuzzy Neural Network
  • Hybrid Evolutionary Learning Algorithms
  • Power System Load Frequency Control using
  • Risk-Management Models

Reviews and Testimonials

While integrating advanced technologies clearly falls in the emerging category because of recency, it is now beginning to reach popularity and more books on this topic becomes desirable. It is hoped that this book will provide a reference to researchers, practitioners, students in both soft computing and data mining communities and others, for the benefit of more creative ideas.

– Leon Shyue-Liang Wang

Table of Contents and List of Contributors

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Editorial Advisory Board
Table of Contents
Ngoc Thanh Nguyen
Leon Shyue-Liang Wang, Tzung-Pei Hong
Chapter 1
J. Alcalá-Fdez, F. Herrera, S. García, M.J. del Jesus, L. Sánchez, E. Bernadó-Mansilla, A. Peregrín, S. Ventura
KEEL is a Data Mining software tool to assess the behaviour of evolutionary learning algorithms in particular and soft computing algorithms in... Sample PDF
Introduction to the Experimental Design in the Data Mining Tool KEEL
Chapter 2
Pei-Wei Tsai, Jeng-Shyang Pan, Bin-Yih Liao, Shu-Chuan Chu, Mei-Chiao Lai
This chapter reviews the basic idea and processes in data mining and some algorithms within the field of evolutionary computing. The authors focus... Sample PDF
Cat Swarm Optimization Supported Data Mining
Chapter 3
Shyue-Liang Wang, Ju-Wen Shen, Tuzng-Pei Hong
Discovery of functional dependencies (FDs) from relational databases has been identified as an important database analysis technique. Various mining... Sample PDF
Dynamic Discovery of Fuzzy Functional Dependencies Using Partitions
Chapter 4
Peitsang Wu, Yung-Yao Hung
In this chapter, a meta-heuristic algorithm (Electromagnetism-like Mechanism, EM) for global optimization is introduced. The Electromagnetism-like... Sample PDF
An Intelligent Data Mining System Through Integration Of Electromagnetism-Like Mechanism And Fuzzy Neural Network
Chapter 5
Yuanyuan Chai
This chapter is a survey of CI and indicates the Simulation Mechanism-Based (SMB) classification method for Computational Intelligence through... Sample PDF
Computational Intelligence-Revisited
Chapter 6
Shangce Gao, Zheng Tang, Hiroki Tamura
Artificial Immune System as a new branch in computational intelligence is the distributed computational technique inspired by immunological... Sample PDF
Artificial Clonal Selection Model and Its Application
Chapter 7
Takashi Hasuike
This chapter considers various types of risk-management models based on the portfolio theory under some social uncertainty that received historical... Sample PDF
Risk-Management Models Based on the Portfolio Theory Using Historical Data under Uncertainty
Chapter 8
Chen-Sen Ouyang
Neuro-fuzzy modeling is a computing paradigm of soft computing and very efficient for system modeling problems. It integrates two well-known... Sample PDF
Neuro-Fuzzy System Modeling
Chapter 9
Shun-Feng Su, Sou-Horng Li
Forecasting data from a time series is to make predictions for the future from available data. Thus, such a problem can be viewed as a traditional... Sample PDF
Network Based Fusion of Global and Local Information in Time Series Prediction with the Use of Soft-Computing Techniques
Chapter 10
Yunong Zhang, Ning Tan
Artificial neural networks (ANN), especially with error back-propagation (BP) training algorithms, have been widely investigated and applied in... Sample PDF
Weights Direct Determination of Feedforward Neural Networks without Iterative BP-Training
Chapter 11
Cha-Hwa Lin, Jin-Fu Wang
Mobile agent planning (MAP) is one of the most important techniques in the mobile computing paradigm to complete a given task in the most efficient... Sample PDF
The Hopfield-Tank Neural Network for the Mobile Agent Planning Problem
Chapter 12
Cheng-Jian Lin, Cheng-Hung Chen
This chapter presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new... Sample PDF
A Novel Neural Fuzzy Network Using a Hybrid Evolutionary Learning Algorithm
Chapter 13
Yannis L. Karnavas
The load frequency control (LFC) is to maintain the power balance in the electrical power system such that the system’s frequency deviates from its... Sample PDF
Power System Load Frequency Control Using Combined Intelligent Techniques
Chapter 14
Daw-Tung Lin, Guan-Jhih Liao
Multimedia products today broadcast over networks and are typically compressed and transmitted from host to client. Adding watermarks to the... Sample PDF
Computational Intelligence Clustering for Dynamic Video Watermarking
Chapter 15
Leszek Borzemski
Data mining (DM) is the key process in knowledge discovery. Many theoretical and practical DM applications can be found in science and engineering.... Sample PDF
Data Mining Meets Internet and Web Performance
Chapter 16
Aparna Konduri, Chien-Chung Chan
As vast numbers of web services have been developed over a broad range of functionalities, it becomes a challenging task to find relevant or similar... Sample PDF
Predicting Similarity of Web Services Using WordNet
Chapter 17
Kazuhiro Seki, Javed Mostafa, Kuniaki Uehara
This chapter discusses two different types of text data mining focusing on the biomedical literature. One deals with explicit information or facts... Sample PDF
Finding Explicit and Implicit Knowledge: Biomedical Text Data Mining
Chapter 18
Yu-Bin Yang, Hui Lin
This chapter presents an automatic meteorological data mining system based on analyzing and mining heterogeneous remote sensed image datasets, with... Sample PDF
Rainstorm Forecasting By Mining Heterogeneous Remote Sensed Datasets
Chapter 19
Sebastian Nusser, Clemens Otte, Werner Hauptmann, Rudolf Kruse
This chapter describes a machine learning approach for classification problems in safety-related domains. The proposed method is based on ensembles... Sample PDF
Learning Verifiable Ensembles for Classification Problems with High Safety Requirements
About the Contributors


Since its inception, data mining has been described as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data". It was usually used by business intelligence organizations and analysts to extract useful information from databases. But increasing applications of data mining have been found in other areas to extract information from the enormous data sets generated by modern experimental and observational methods. However, due to the intractable computational complexity of many existing data mining techniques for real world problems, techniques that are tolerable to imprecision, uncertainty, and approximation are very desirable.

In contrast to conventional hard computing, the basic ideas underlying soft computing is to exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. At this juncture, the principal constituents of soft computing are Fuzzy Logic (FL), Neural Computing (NC), Evolutionary Computation (EC), Genetic Algorithms (GA), Swam Intelligence (SI), Machine Learning (ML) and Probabilistic Reasoning (PR), with the latter subsuming belief networks, chaos theory and parts of learning theory. It has been demonstrated in many areas that the soft computing methodologies are complementary to many existing theories and technologies.

As such, the objective of this book is to present an international forum for the synergy of new developments from two different research disciplines. It is hoped that through the fusion of diverse techniques and applications, new and innovative ideas will be stimulated and shared.

This book contains nineteen chapters written by leading experts from researchers of soft computing and data mining communities as well as practitioners from medical science, space and geo-information science, innovative life science, and traffic and transportation engineering. The book is organized into three sections. The first section shows four innovative works that give a flavor of how soft computation and data mining can be integrated for various applications. The second section compiles nine new soft computation techniques for different real world applications, with a leading chapter of survey to classify current computational intelligence technologies. The third section is devoted to five real life problems that can be addressed by the proposed new data mining techniques. Since the chapters are written by many researchers with different backgrounds around the world, the topics and content covered in this book provides insights which are not easily accessible otherwise.

While integrating advanced technologies clearly falls in the emerging category because of recency, it is now beginning to reach popularity and more books on this topic becomes desirable. It is hoped that this book will provide a reference to researchers, practitioners, students in both soft computing and data mining communities and others, for the benefit of more creative ideas.

We are grateful to all authors for their contributions and the referees for their vision and efforts. We would like to express our thanks to IGI Global and National University of Kaohsiung for realizing this book project.

Author(s)/Editor(s) Biography

Leon Shyue-Liang Wang received his Ph.D. from State University of New York at Stony Brook in 1984. From 1984 to 1987, he joined the University of New Haven as assistant professor. From 1987 to 1994, he joined New York Institute of Technology as assistant/associate professor. From 1994 to 2002, he joined I-Shou University in Taiwan and served as Director of Computing Center, Director of Library, and Chairman of Information Management Department. In 2002, he joined National University of Kaohsiung, Taiwan. In 2003, he rejoined NYIT. He is now professor and chairman in National University of Kaohsiung, Taiwan. He has published over 150 papers in the areas of data mining and soft computing, and served as a PC member of several national and international conferences. He is a member of the board of Chinese American Academic and Professional Society, USA.
Tzung-Pei Hong received his B.S. degree in chemical engineering from National Taiwan University in 1985, and his Ph.D. degree in computer science and information engineering from National Chiao-Tung University in 1992. He was a faculty at the Department of Computer Science in Chung-Hua Polytechnic Institute from 1992 to 1994, and at the Department of Information Management in I-Shou University from 1994 to 2001. Since 2001, he has served as Director of Library and Computing Center, Dean of Academic Affair, and Vice President of National University of Kaohsiung. He is currently a distinguished professor at the Department of Electrical Engineering and the department of Computer Science and Information Engineering in National University of Kaohsiung. His current research interests include machine learning, data mining, soft computing, management information systems, WWW applications and has published more than 300 technical papers.


Editorial Board

  • Shyi-Ming Chen, National Taiwan University of Science and Technology, Taiwan
  • Arbee L. P. Chen, National Chengchi University, Taiwan
  • Saman Halgamuge, University of Melbourne, Australia
  • Francisco Herrera, University of Granada, Spain
  • Hisao Ishibuchi, Osaka Prefecture University, Japan
  • Tsau-Young Lin, San Jose State University, USA
  • Wen-Yang Lin, National University of Kaohsiung, Taiwan
  • Vincenzo Loia, University of Salerno, Italy
  • Ngoc Thanh Nguyen, Wroclaw University of Technology, Poland
  • Shusaku Tsumoto, Shimane University, Japan