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Heuristic and Optimization for Knowledge Discovery

Release Date: July, 2001. Copyright © 2002. 296 pages.
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DOI: 10.4018/978-1-93070-826-6, ISBN13: 9781930708266, ISBN10: 1930708262, EISBN13: 9781591400172
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MLA

Abbass, Hussein A., Charles S. Newton and Ruhul Sarker . "Heuristic and Optimization for Knowledge Discovery." IGI Global, 2002. 1-296. Web. 21 May. 2013. doi:10.4018/978-1-93070-826-6

APA

Abbass, H. A., Newton, C. S., & Sarker , R. (2002). Heuristic and Optimization for Knowledge Discovery (pp. 1-296). doi:10.4018/978-1-93070-826-6

Chicago

Abbass, Hussein A., Charles S. Newton and Ruhul Sarker . "Heuristic and Optimization for Knowledge Discovery." 1-296 (2002), accessed May 21, 2013. doi:10.4018/978-1-93070-826-6

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Heuristic and Optimization for Knowledge Discovery
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Description

With the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic techniques. Heuristic and Optimization for Knowledge Discovery addresses the foundation of this topic, as well as its practical uses, and aims to fill in the gap that exists in current literature.

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Table of Contents and List of Contributors

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1.
R. Sarker (University of New South Wales, Australia), H. Abbass (University of New South Wales, Australia), C. Newton (University of New South Wales, Australia)
The terms Data Mining (DM) and Knowledge Discovery in Databases (KDD) have been used interchangeably in practice. Strictly speaking, KDD is the umbrella of the minin... Sample PDF | More details...
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2.
A. M. Bagirov (University of Ballarat, Australia), A. M. Rubinov (University of Ballarat, Australia), J. Yearwood (University of Ballarat, Australia)
The feature selection problem involves the selection of a subset of features that will be sufficient for the determination of structures or clusters in a given datas... Sample PDF | More details...
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3.
Kai Ming Ting (Monash University, Australia)
This chapter reports results obtained from a series of studies on costsensitive classification using decision trees, boosting algorithms, and MetaCost which is a rec... Sample PDF | More details...
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4.
Agapito Ledezma (Universidad Carlos III de Madrid, Spain), Ricardo Aler (Universidad Carlos III de Madrid, Spain), Daniel Borrajo (Universidad Carlos III de Madrid, Spain)
Currently, the combination of several classifiers is one of the most active fields within inductive learning. Examples of such techniques are boosting, bagging and s... Sample PDF | More details...
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5.
Craig M. Howard (Lanner Group Ltd., UK)
The overall size of software packages has grown considerably over recent years. Modular programming, object-oriented design and the use of static and dynamic librari... Sample PDF | More details...
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6.
Jose Ruiz-Shulcloper (University of Tennessee, USA), Guillermo Sanchez-Diaz (Autonomous University of Hidalgo State, Mexico), Mongi A. Abidi (University of Tennessee, USA)
In this chapter, we expose the possibilities of the Logical Combinatorial Pattern Recognition (LCPR) tools for Clustering Large and Very Large Mixed Incomplete Data... Sample PDF | More details...
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7.
Bayesian Learning (pages 108-121)
Paula Macrossan (University of New England, Australia), Kerrie Mengersen (University of Newcastle, Australia)
Learning from the Bayesian perspective can be described simply as the modification of opinion based on experience. This is in contrast to the Classical or “frequenti... Sample PDF | More details...
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8.
Paul D. Scott (University of Essex, UK)
This chapter addresses the question of how to decide how large a sample is necessary in order to apply a particular data mining procedure to a given data set. A brie... Sample PDF | More details...
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9.
The Gamma Test (pages 142-167)
Antonia J. Jones (Cardiff University, UK), Dafydd Evans (Cardiff University, UK), Steve Margetts (Cardiff University, UK), Peter J. Durrant (Cardiff University, UK)
The Gamma Test is a non-linear modelling analysis tool that allows us to quantify the extent to which a numerical input/output data set can be expressed as a smooth... Sample PDF | More details...
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10.
Hyeyoung Park (Brain Science Institute, Japan)
Feed forward neural networks or multilayer perceptrons have been successfully applied to a number of difficult and diverse applications by using the gradient descent... Sample PDF | More details...
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11.
Kevin E. Voges (Griffith University, Australia), Nigel K.L. Pope (Griffith University, Australia), Mark R. Brown (Griffith University, Australia)
Cluster analysis is a common market segmentation technique, usually using k-means clustering. Techniques based on developments in computational intelligence are incr... Sample PDF | More details...
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12.
Susan E. George (University of South Australia, Australia)
This chapter presents a survey of medical data mining focusing upon the use of heuristic techniques. We observe that medical mining has some unique ethical issues be... Sample PDF | More details...
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13.
A. de Carvalho (University of Guelph, Canada), A. P. Braga (Federal University of Minas Gerais, Brazil), S. O. Rezende (University of Sao Paulo, USA), E. Martineli (University of Sao Paulo, USA), T. Ludermir (Federal University of Pernambuco, Brazil)
In the last few years, a large number of companies are starting to realize the value of their databases. These databases, which usually cover transactions performed... Sample PDF | More details...
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14.
Alina Lazar (Wayne State University, USA)
The goal of this research is to investigate and develop heuristic tools in order to extract meaningful knowledge from archeological large-scale data sets. Database q... Sample PDF | More details...
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15.
Denny Meyer (Massey University, New Zealand), Andrew Balemi (Colmar Brunton Research, New Zealand), Chris Wearing (Colmar Brunton Research, New Zealand)
Neural networks are commonly used for prediction and classification when data sets are large. They have a big advantage over conventional statistical tools in that i... Sample PDF | More details...
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Author(s)/Editor(s) Biography

Hussein A. Abbass is the director of the Artificial Life and Adaptive Robotics Laboratory at the School of Information Technology and Electrical Engineering at the Australian Defense Force Academy campus of the University of New South Wales. Dr. Abbass is a Senior Member of the IEEE and has more than 15 years experience in industry and academia and more than a hundred fully refereed papers in international journals and conferences. He teaches computational intelligence related subjects and his research focuses on multi-agent systems, data mining, and artificial life models with applications to defence, security and business.
Charles S. Newton is the Head of Computer Science, University of New South Wales (UNSW) at the Australian Defence Force Academy (ADFA) campus, Canberra. Prof. Newton is also the Deputy Rector (Education). He obtained his Ph.D. in Nuclear Physics from the Australian National University, Canberra in 1975. He joined the School of Computer Science in 1987 as a Senior Lecturer in Operations Research. In May 1993, he was appointed Head of School and became Professor of Computer Science in November 1993. Prior to joining at ADFA, Prof. Newton spent nine years in the Analytical Studies Branch of the Department of Defence. In 1989-91, Prof. Newton was the National President of the Australian Society for Operations Research. His Research Interests encompass Group Decision Support Systems, Simulation, Wargaming, Evolutionary Computation, Data Mining and Operations Research Applications. He has published extensively in national and international journals, books and conference proceedings.
Ruhul Sarker received his Ph.D. in 1991 from DalTech, Dalhousie University, Halifax, Canada, and is currently a Senior Lecturer in Operations Research at the School of Computer Science, University of New South Wales, ADFA Campus, Canberra, Australia. Before joining at UNSW in February 1998, Dr Sarker worked with Monash University, Victoria, and the Bangladesh University of Engineering and Technology, Dhaka. His main research interests are Evolutionary Optimization, Data Mining and Applied Operations Research. He is currently involved with four edited books either as editor or co-editor, and has published more than 60 refereed papers in international journals and conference proceedings. He is also the editor of ASOR Bulletin, the national publication of the Australian Society for Operations Research.