About Book

There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyze this huge amount of data and extract useful information from it. Data mining can be defined as a process of exploring and analysis for large amounts of data with a specific target on discovering significantly important patterns and rules. Data mining helps finding knowledge from raw, unprocessed data. Data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. Data mining is highly useful in market analysis, management corporate analysis & risk management fraud detection apart from these; data mining can also be used in the areas of production control, customer retention, science exploration, sports and internet web surf-aid. This book attempts to understand the multiple branches that fall under the discipline of data mining and how such concepts have practical applications. It deals with interaction techniques, project management, spectral data mining, large-scale data mining, cognitive radio systems and data mining approaches. Coherent flow of topics, student-friendly language and extensive use of examples make this book an invaluable source of knowledge. As this field is emerging at a rapid pace, the contents of this book will help the readers understand the modern concepts and applications of the subject.

Preface............................................................................................................v

Chapter 1 Consumer Life Cycle and Profiling: A Data Mining Perspective...1

Abstract...................................................................................................1

1. Introduction............................................................................................2

2. Customer lifecycle.................................................................................2

3. Customer analytics..............................................................................13

4. Conclusion............................................................................................17

Acknowledgements.............................................................................18

Chapter 2 Data Mining Approaches for Understanding of Regulation of Expression of the Urea Cycle Genes...................................................20

Abstract.................................................................................................20

1. Introduction..........................................................................................20

2. Transcriptional regulation of urea cycle genes.............................25

Acknowledgements.............................................................................34

Conflict of interest..............................................................................34

Chapter 3 A Survey of Data Mining Techniques for Steganalysis.................43

1. Introduction..........................................................................................43

2. General categorization of data mining techniques on

steganalysis...........................................................................................44

3. Overview of data mining techniques..............................................46

4. Data mining techniques on steganalysis over the image...........47

5. Data mining techniques on steganalysis over multimedia and

protocol.................................................................................................55

6. Discussion and analysis.....................................................................58

7. Conclusion and future work.............................................................63

Chapter 4 Data Mining Applied to Cognitive Radio Systems.........................71

1. Introduction..........................................................................................71

3. Front end: Cyclostationarity.............................................................73

4. Classifiers..............................................................................................75

5. Results...................................................................................................80

6. Simulation result of a multipath rayleigh fading and

AWGN channel....................................................................................89

7. Conclusions..........................................................................................91

Chapter 5 Analysis and Learning Frameworks for Large-Scale Data

Mining.........................................................................................................94

1. Introduction..........................................................................................94

2. Architecture..........................................................................................98

3. Tree-structured data analysis framework....................................100

4. Parallel machine learning framework...........................................105

5. Conclusion..........................................................................................110

Chapter 6 Data Mining and Neural Networks: The Impact of Data

Representation.........................................................................................113

1. Introduction ......................................................................................113

2. Data mining ......................................................................................114

3. Data representation .........................................................................115

4. Methodology .....................................................................................116

5. Results ................................................................................................128

6. Conclusion and future research ....................................................131

Chapter 7 An Unsupervised Classification Method for Hyperspectral Remote

Sensing Image Based on Spectral Data Mining.............................137

1. Introduction........................................................................................137

2. The study area .................................................................................138

3. Remote sensing data .......................................................................139

4. Methodology .....................................................................................141

5. Results and discussions ..................................................................145

Acknowledgement ...........................................................................145

Chapter 8 BotNet Detection: Enhancing Analysis by Using Data Mining

Techniques................................................................................................149

1. Introduction ......................................................................................149

2. Classification of Botnets...................................................................151

3. Botnet attacks.....................................................................................153

4. Botnet analysis .................................................................................155

5. Botnet detection ...............................................................................159

6. Conclusion .........................................................................................166

Chapter 9 Data Mining Applied to the Improvement of Project

Management.............................................................................................172

1. Introduction ......................................................................................172

2. Understanding the software project environment ....................175

3. Related works and assess situation .............................................179

4. Application process and guidelines .............................................181

5. Application case ...............................................................................182

6. Discussion .........................................................................................195

7. Conclusion .......................................................................................199

Chapter 10 An Overview of Interaction Techniques and 3D Representations

for Data Mining......................................................................................204

1. Introduction........................................................................................204

2. Visual Data Mining...........................................................................206

3. From 2D to 3D visualization and virtual reality........................209

4. Visual representations for Visual Data Mining..........................211

5. Interaction techniques for Visual Data Mining...........................216

6. Related work on taxonomies of visual representations and

interaction techniques.......................................................................221

7. A new classification of Visual Data Mining based on visual

representations and interaction techniques.................................222

8. Conclusion..........................................................................................231

Citation......................................................................................................239

Index...........................................................................................................241

About Author

Micah Haldane obtained his Ph.D. in Computer Science from Public Technical University, Melbourne. His research interests include big data, business intelligence and predictive analytics, data and information science, information retrieval, XML, web services and service-oriented architectures, and relational and NoSQL database systems. He is working as an Associate Professor at Open Research University in Skopje. Haldane has contributed over 35 journal and conference papers and also serves as a journal and conference reviewer. He has been an eminent speaker in seminars across Europe and Asia.