ADVANCES IN DATA MINING KNOWLEDGE DISCOVERY AND APPLICATIONS Edited by Adem Karahoca Advances in Data Mining Knowledge Discovery and Applications http://dx.doi.org/10.5772/3349 Edited by Adem Karahoca Contributors Dost Muhammad Khan, Nawaz Mohamudally, D. K. R. Babajee, Tomas Borovicka, Marcel Jirina Jr., Pavel Kordik, Marcel Jirina, Carmelo Cassisi, Placido Montalto, Marco Aliotta, Andrea Cannata, Alfredo Pulvirenti, Fadzilah Siraj, Ehab A. Omer A. Omer, Md. Rajib Hasan, Mohammad Lutfi Othman, Ishak Aris, Xingping Wen, Xiaofeng Yang, Maria Madalena Dias, Juliana Keiko Yamaguchi, Emerson Rabelo, Clélia Franco, Kohsuke Yanai, Toshihiko Yanase, Lilian Freitas, Yomara Pires, Jefferson Morais, João Costa, Aldebaro Klautau, José C. Reston Filho, Carolina de M. Affonso, Roberto Célio L. de Oliveira, Intan Azmira binti Wan Abdul Razak, Shah bin Majid, Mohd Shahrieel bin Mohd. Aras, Arfah binti Ahmad, Ghulam Mujtaba Shaikh, Tariq Mahmood, Matthew Watts, Robert Carrese, Hadi Winarto, R.L.K.Venkateswarlu, R. Raviteja, R. Rajeev, Gao Jie, Dewan Md. Farid, Mohammad Zahidur Rahman, Chowdhury Mofizur Rahman, Erdem Alparslan, Adem Karahoca, Dilek Karahoca, Adela Bâra, Ion Lungu Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Mirna Cvijic Typesetting InTech Prepress, Novi Sad Cover InTech Design Team First published September, 2012 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechopen.com Advances in Data Mining Knowledge Discovery and Applications, Edited by Adem Karahoca p. cm. ISBN 978-953-51-0748-4 Contents Preface IX Section 1 Knowledge Discovery 1 Chapter 1 Towards the Formulation of a Unified Data Mining Theory, Implemented by Means of Multiagent Systems (MASs) 3 Dost Muhammad Khan, Nawaz Mohamudally and D. K. R. Babajee Chapter 2 Selecting Representative Data Sets 43 Tomas Borovicka, Marcel Jirina Jr., Pavel Kordik and Marcel Jirina Chapter 3 Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining 71 Carmelo Cassisi, Placido Montalto, Marco Aliotta, Andrea Cannata and Alfredo Pulvirenti Chapter 4 Data Mining and Neural Networks: The Impact of Data Representation 97 Fadzilah Siraj, Ehab A. Omer A. Omer and Md. Rajib Hasan Chapter 5 Inconsistent Decision System: Rough Set Data Mining Strategy to Extract Decision Algorithm of a Numerical Distance Relay – Tutorial 117 Mohammad Lutfi Othman and Ishak Aris Chapter 6 An Unsupervised Classification Method for Hyperspectral Remote Sensing Image Based on Spectral Data Mining 143 Xingping Wen and Xiaofeng Yang Chapter 7 Visualization Techniques: Which is the Most Appropriate in the Process of Knowledge Discovery in Data Base? 155 Maria Madalena Dias, Juliana Keiko Yamaguchi, Emerson Rabelo and Clélia Franco Chapter 8 Analysis and Learning Frameworks for Large-Scale Data Mining 181 Kohsuke Yanai and Toshihiko Yanase VI Contents Section 2 Data Mining Applications 197 Chapter 9 Data Mining Applied to Cognitive Radio Systems 199 Lilian Freitas, Yomara Pires, Jefferson Morais, João Costa and Aldebaro Klautau Chapter 10 Short-Term Energy Price Prediction Multi-Step-Ahead in the Brazilian Market Using Data Mining 219 José C. Reston Filho, Carolina de M. Affonso and Roberto Célio L. de Oliveira Chapter 11 Electricity Load Forecasting Using Data Mining Technique 235 Intan Azmira binti Wan Abdul Razak, Shah bin Majid, Mohd Shahrieel bin Mohd. Aras and Arfah binti Ahmad Chapter 12 Mining and Adaptivity in Automated Teller Machines 255 Ghulam Mujtaba Shaikh and Tariq Mahmood Chapter 13 Data Mining for Motorsport Aerodynamics 281 Matthew Watts, Robert Carrese and Hadi Winarto Chapter 14 The Performance Evaluation of Speech Recognition by Comparative Approach 311 R.L.K. Venkateswarlu, R. Raviteja and R. Rajeev Chapter 15 Data Mining from Remote Sensing Snow and Vegetation Product 337 Gao Jie Chapter 16 Mining Complex Network Data for Adaptive Intrusion Detection 357 Dewan Md. Farid, Mohammad Zahidur Rahman and Chowdhury Mofizur Rahman Chapter 17 BotNet Detection: Enhancing Analysis by Using Data Mining Techniques 379 Erdem Alparslan, Adem Karahoca and Dilek Karahoca Chapter 18 Improving Decision Support Systems with Data Mining Techniques 397 Adela Bâra and Ion Lungu Preface Advances in Knowledge Discovery and Data Mining aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications. First part of the book consists of the first and second phases of the knowledge discovery. Second part focuses on the data mining applications from energy, electricity, speech recognition and network security. Section 1. Knowledge Discovery Chapter 1 presents the mathematical formulation of unified theory of data mining and multi agent system architecture. The data mining processes clustering, classification and visualization are unified by means of mathematical functions and multi agent system is developed (Khan, et al.). Chapter 2 focuses on an overview of existing methods that deal with methods of data selection and sampling (Borovicka, et al.). Chapter 3 provides an overview of main dimensionality reduction algorithms, together with a detailed description of the most used similarity measures in time series data mining (Cassisi, et al.). Chapter 4 explorers the effect of different data representations on the performance of neural network and regression was investigated on different datasets that have binary or Boolean class target (Siraj, et al.). X Preface Chapter 5 proposes employing rough set theory to obtain the knowledge about the distance protective relay under supervised learning (Othman and Aris). Chapter 6 presents an unsupervised classification for hyperspectral remote sensing image. It can effectively extract the low reflectance object such as vegetation in shadowed region or water from hyperspectral image using spectral data mining (Wen, et al.). Chapter 7 demonstrates that there are some parameters to be considered in the choice of visualization techniques, which are: data type, task type, data scalability, data dimension and position of the attributes in the display. Also presents the application of geometrical and iconographic techniques over results of clustering algorithm with the objective of illustrate the contribution of the guidelines (Madalena, et al.). Chapter 8 proposes two computing frameworks for large-scale data mining via tree structured data analysis framework and parallel machine learning framework (Yanai and Yanase). Section 2. Data Mining Applications Chapter 9 discusses the task of modulation classification in cognitive radio. The modulation classification becomes fundamental, since this information allows the RC to adapt its transmission parameters for the spectrum to be shared efficiently, without causing interference to other users (Freitas, et al.). Chapter 10 proposes a methodology combining ARIMA and Artificial Neural Network (ANN) for short-term energy price prediction multi-step-ahead in the Brazilian market (Filho, et al.). Chapter 11 offers data mining application for short term electricity load forecasting to organize supply and demand fluctuations (Razak, et al.). Chapter 12 proposes a set of five adaptive ATM interfaces, which are adapted to the behavior of an ATM customer population (Shaikh and Mahmood). Chapter 13 details a design space exploration upon a low speed aerofoil section for the front wing of a Formula One vehicle. In order to obtain a complete understanding of the design space, two data mining techniques were considered. For qualitative information Self-Organising Map (SOM) was applied while Analysis Of Variance (ANOVA) was used to highlight the quantitative response (Watts, et al.). Chapter 14 benchmarks the different data mining techniques to obtain best speech recognition (Venkateswarlu, et al.). Chapter 15 suggests remote sensing data. Elevation as a variable is considered in spatial distribution of snow, vegetation will be used as an indicator. Also chapter investigates (1) quantification of SCA (Snow-Cover Area per unit area of elevation . ADVANCES IN DATA MINING KNOWLEDGE DISCOVERY AND APPLICATIONS Edited by Adem Karahoca Advances in Data Mining Knowledge Discovery and Applications http://dx.doi.org/10.5772/3349. in the field of data mining research which should be addressed. These problems are: Unified Data mining Processes, Scalability, Mining Unbalanced, Complex and Multiagent Data, Data mining in. Cycle Advances in Data Mining Knowledge Discovery and Applications 6 The figure 2 is the proposed unified data mining life cycle. The first three processes of unified data mining processes