Data Mining, a Heuristic Approach

310 Pages·2002·1.835 MB·English

by  Hussein A. Abbass, Ruhul Amin Sarker, Charles S. Newton

Data Mining:
A Heuristic Approach
Hussein A. Abbass
Ruhul A. Sarker
Charles S. Newton
University of New South Wales, Australia
Idea Group Information Science
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Library of Congress Cataloging-in-Publication Data
Data mining : a heuristic approach / [edited by] Hussein Aly Abbass, Ruhul Amin
Sarker, Charles S. Newton.
p. cm.
Includes index.
ISBN 1-930708-25-4
1. Data mining. 2. Database searching. 3. Heuristic programming. I. Abbass, Hussein.
II. Sarker, Ruhul. III. Newton, Charles, 1942-
QA76.9.D343 D36 2001
006.31--dc21 2001039775
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
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Data Mining: A Heuristic Approach
Table of Contents
Preface ............................................................................................................................vi
Part One: General Heuristics
Chapter 1: From Evolution to Immune to Swarm to …?
A Simple Introduction to Modern Heuristics....................................................... 1
Hussein A. Abbass, University of New South Wales, Australia
Chapter 2: Approximating Proximity for Fast and Robust
Distance-Based Clustering ................................................................................22
Vladimir Estivill-Castro, University of Newcastle, Australia
Michael Houle, University of Sydney, Australia
Part Two: Evolutionary Algorithms
Chapter 3: On the Use of Evolutionary Algorithms in Data Mining..........................48
Erick Cantú-Paz, Lawrence Livermore National Laboratory, USA
Chandrika Kamath, Lawrence Livermore National Laboratory, USA
Chapter 4: The discovery of interesting nuggets using heuristic techniques..........72
Beatriz de la Iglesia, University of East Anglia, UK
Victor J. Rayward-Smith, University of East Anglia, UK
Chapter 5: Estimation of Distribution Algorithms for Feature Subset
Selection in Large Dimensionality Domains.....................................................97
Iñaki Inza, University of the Basque Country, Spain
Pedro Larrañaga, University of the Basque Country, Spain
Basilio Sierra, University of the Basque Country, Spain
Chapter 6: Towards the Cross-Fertilization of Multiple Heuristics:
Evolving Teams of Local Bayesian Learners ...................................................117
Jorge Muruzábal, Universidad Rey Juan Carlos, Spain
Chapter 7: Evolution of Spatial Data Templates for Object Classification..............143
Neil Dunstan, University of New England, Australia
Michael de Raadt, University of Southern Queensland, Australia
Part Three: Genetic Programming
Chapter 8: Genetic Programming as a Data-Mining Tool .......................................157
Peter W.H. Smith, City University, UK
Chapter 9: A Building Block Approach to Genetic Programming
for Rule Discovery.............................................................................................174
A.P. Engelbrecht, University of Pretoria, South Africa
Sonja Rouwhorst, Vrije Universiteit Amsterdam, The Netherlands
L. Schoeman, University of Pretoria, South Africa
Part Four: Ant Colony Optimization and Immune Systems
Chapter 10: An Ant Colony Algorithm for Classification Rule Discovery .............191
Rafael S. Parpinelli, Centro Federal de Educacao Tecnologica do Parana, Brazil
Heitor S. Lopes, Centro Federal de Educacao Tecnologica do Parana, Brazil
Alex A. Freitas, Pontificia Universidade Catolica do Parana, Brazil
Chapter 11: Artificial Immune Systems: Using the Immune System
as Inspiration for Data Mining .........................................................................209
Jon Timmis, University of Kent at Canterbury, UK
Thomas Knight, University of Kent at Canterbury, UK
Chapter 12: aiNet: An Artificial Immune Network for Data Analysis....................231
Leandro Nunes de Castro, State University of Campinas, Brazil
Fernando J. Von Zuben, State University of Campinas, Brazil
Part Five: Parallel Data Mining
Chapter 13: Parallel Data Mining.............................................................................261
David Taniar, Monash University, Australia
J. Wenny Rahayu, La Trobe University, Australia
About the Authors......................................................................................................290
Index ...........................................................................................................................297
vi
Preface
The last decade has witnessed a revolution in interdisciplinary research where the
boundaries of different areas have overlapped or even disappeared. New fields of research
emerge each day where two or more fields have integrated to form a new identity. Examples
of these emerging areas include bioinformatics (synthesizing biology with computer and
information systems), data mining (combining statistics, optimization, machine learning,
artificial intelligence, and databases), and modern heuristics (integrating ideas from tens of
fields such as biology, forest, immunology, statistical mechanics, and physics to inspire
search techniques). These integrations have proved useful in substantiating problem-
solving approaches with reliable and robust techniques to handle the increasing demand from
practitioners to solve real-life problems. With the revolution in genetics, databases, automa-
tion, and robotics, problems are no longer those that can be solved analytically in a feasible
time. Complexity arises because of new discoveries about the genome, path planning,
changing environments, chaotic systems, and many others, and has contributed to the
increased demand to find search techniques that are capable of getting a good enough
solution in a reasonable time. This has directed research into heuristics.
During the same period of time, databases have grown exponentially in large stores and
companies. In the old days, system analysts faced many difficulties in finding enough data
to feed into their models. The picture has changed and now the reverse picture is a daily
problem–how to understand the large amount of data we have accumulated over the years.
Simultaneously, investors have realized that data is a hidden treasure in their companies. With
data, one can analyze the behavior of competitors, understand the system better, and
diagnose the faults in strategies and systems. Research into statistics, machine learning, and
data analysis has been resurrected. Unfortunately, with the amount of data and the complexity
of the underlying models, traditional approaches in statistics, machine learning, and tradi-
tional data analysis fail to cope with this level of complexity. The need therefore arises for
better approaches that are able to handle complex models in a reasonable amount of time.
These approaches have been named data mining (sometimes data farming) to distinguish
them from traditional statistics, machine learning, and other data analysis techniques. In
addition, decision makers were not interested in techniques that rely too much on the
underlying assumptions in statistical models. The challenge is to not have any assumptions
about the model and try to come up with something new, something that is not obvious or
predictable (at least from the decision makers’ point of view). Some unobvious thing may have
significant values to the decision maker. Identifying a hidden trend in the data or a buried fault
in the system is by all accounts a treasure for the investor who knows that avoiding loss
results in profit and that knowledge in a complex market is a key criterion for success and
continuity. Notwithstanding, models that are free from assumptions–or at least have
minimum assumptions–are expensive to use. The dramatic search space cannot be navigated
using traditional search techniques. This has highlighted a natural demand for the use of
heuristic search methods in data mining.
This book is a repository of research papers describing the applications of modern
vii
heuristics to data mining. This is a unique–and as far as we know, the first–book that provides
up-to-date research in coupling these two topics of modern heuristics and data mining.
Although it is by all means an incomplete coverage, it does provide some leading research
in this area.
This book contains open-solicited and invited chapters written by leading researchers in
the field. All chapters were peer reviewed by at least two recognized researchers in the field
in addition to one of the editors. Contributors come from almost all the continents and
therefore, the book presents a global approach to the discipline. The book contains 13
chapters divided into five parts as follows:
• Part 1: General Heuristics
• Part 2: Evolutionary Algorithms
• Part 3: Genetic Programming
• Part 4: Ant Colony Optimization and Immune Systems
• Part 5: Parallel Data Mining
Part 1 gives an introduction to modern heuristics as presented in the first chapter. The
chapter serves as a textbook-like introduction for readers without a background in heuristics
or those who would like to refresh their knowledge.
Chapter 2 is an excellent example of the use of hill climbing for clustering. In this chapter,
Vladimir Estivill-Castro and Michael E. Houle from the University of Newcastle and the
University of Sydney, respectively, provide a methodical overview of clustering and hill
climbing methods to clustering. They detail the use of proximity information to assess the
scalability and robustness of clustering.
Part 2 covers the well-known evolutionary algorithms. After almost three decades of
continuous research in this area, the vast amount of papers in the literature is beyond a single
survey paper. However, in Chapter 3, Erick Cantú-Paz and Chandrika Kamath from Lawrence
Livermore National Laboratory, USA, provide a brave and very successful attempt to survey
the literature describing the use of evolutionary algorithms in data mining. With over 75
references, they scrutinize the data mining process and the role of evolutionary algorithms
in each stage of the process.
In Chapter 4, Beatriz de la Iglesia and Victor J. Rayward-Smith, from the University of East
Anglia, UK, provide a superb paper on the application of Simulated Annealing, Tabu Search,
and Genetic Algorithms (GA) to nugget discovery or classification where an important class
is under-represented in the database. They summarize in their chapter different measures of
performance for the classification problem in general and compare their results against 12
classification algorithms.
Iñaki Inza, Pedro Larrañaga, and Basilio Sierra from the University of the Basque Country,
Spain, follow, in Chapter 5, with an outstanding piece of work on feature subset selection
using a different type of evolutionary algorithms, the Estimation of Distribution Algorithms
(EDA). In EDA, a probability distribution of the best individuals in the population is
maintained to sample the individuals in subsequent generations. Traditional crossover and
mutation operators are replaced by the re-sampling process. They applied EDA to the Feature
Subset Selection problem and showed that it significantly improves the prediction accuracy.
In Chapter 6, Jorge Muruzábal from the University of Rey Juan Carlos, Spain, presents the
brilliant idea of evolving teams of local Bayesian learners. Bayes theorem was resurrected as
a result of the revolution in computer science. Nevertheless, Bayesian approaches, such as
viii
Bayesian Networks, require large amounts of computational effort, and the search algorithm
can easily become stuck in a local minimum. Dr. Muruzábal combined the power of the
Bayesian approach with the ability of Evolutionary Algorithms and Learning Classifier
Systems for the classification process.
Neil Dunstan from the University of New England, and Michael de Raadt from the
University of Southern Queensland, Australia, provide an interesting application of the use
of evolutionary algorithms for the classification and detection of Unexploded Ordnance
present on military sites in Chapter 7.
Part 3 covers the area of Genetic Programming (GP). GP is very similar to the traditional
GA in its use of selection and recombination as the means of evolution. Different from GA,
GP represents the solution as a tree, and therefore the crossover and mutation operators are
adopted to handle tree structures. This part starts with Chapter 8 by Peter W.H. Smith from
City University, UK, who provides an interesting introduction to the use of GP for data mining
and the problems facing GP in this domain. Before discarding GP as a useful tool for data
mining, A.P. Engelbrecht and L Schoeman from the University of Pretoria, South Africa along
with Sonja Rouwhorst from the University of Vrije, The Netherlands, provide a building block
approach to genetic programming for rule discovery in Chapter 9. They show that their
proposed GP methodology is comparable to the famous C4.5 decision tree classifier–a famous
decision tree classifier.
Part 4 covers the increasingly growing areas of Ant Colony Optimization and Immune
Systems. Rafael S. Parpinelli and Heitor S. Lopes from Centro Federal de Educacao Tecnologica
do Parana, and Alex A. Freitas from Pontificia Universidade Catolica do Parana, Brazil, present
a pioneer attempt, in Chapter 10, to apply ant colony optimization to rule discovery. Their
results are very promising and through an extremely interesting approach, they present their
techniques.
Jon Timmis and Thomas Knight, from the University of Kent at Canterbury, UK, introduce
Artificial Immune Systems (AIS) in Chapter 11. In a notable presentation, they present the
AIS domain and how can it be used for data mining. Leandro Nunes de Castro and Fernando
J. Von Zuben, from the State University of Campinas, Brazil, follow in Chapter 12 with the use
of AIS for clustering. The chapter presents a remarkable metaphor for the use of AIS with an
outstanding potential for the proposed algorithm.
In general, the data mining task is very expensive, whether we are using heuristics or any
other technique. It was therefore impossible not to present this book without discussing
parallel data mining. This is the task carried out by David Taniar from Monash University and
J. Wenny Rahayu from La Trobe University, Australia, in Part 5, Chapter 13. They both have
written a self-contained and detailed chapter in an exhilarating style, thereby bringing the
book to a close.
It is hoped that this book will trigger great interest into data mining and heuristics, leading
to many more articles and books!
ix
Acknowledgments
We would like to express our gratitude to the contributors without whose submissions
this book would not have been born. We owe a great deal to the reviewers who reviewed entire
chapters and gave the authors and editors much needed guidance. Also, we would like to
thank those dedicated reviewers, who did not contribute through authoring chapters to the
current book or to our second book Heuristics and Optimization for Knowledge Discovery–
Paul Darwen, Ross Hayward, and Joarder Kamruzzaman.
A further special note of thanks must go also to all the staff at Idea Group Publishing,
whose contributions throughout the whole process from the conception of the idea to final
publication have been invaluable. In closing, we wish to thank all the authors for their insights
and excellent contributions to this book. In addition, this book would not have been possible
without the ongoing professional support from Senior Editor Dr. Mehdi Khosrowpour,
Managing Editor Ms. Jan Travers and Development Editor Ms. Michele Rossi at Idea Group
Publishing. Finally, we want to thank our families for their love, support, and patience
throughout this project.
Hussein A. Abbass, Ruhul Sarker, and Charles Newton
Editors (2001)