Advanced Techniques in Knowledge Discovery and Data Mining

1. Front Matter

   Pages i-xii

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2. Trends in Data Mining and Knowledge Discovery

   • Krzysztof J. Cios, Lukasz A. Kurgan

   Pages 1-26

3. Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data

   • Andreas König, Achim Gratz

   Pages 27-74

4. Clustering and Visualization of Retail Market Baskets

   • Joydeep Ghosh, Alexander Strehl

   Pages 75-102

5. Segmentation of Continuous Data Streams Based on a Change Detection Methodology

   • Gil Zeira, Mark Last, Oded Maimon

   Pages 103-126

6. Instance Selection Using Evolutionary Algorithms: An Experimental Study

   • José Ramón Cano, Francisco Herrera, Manuel Lozano

   Pages 127-152

7. Using Cooperative Coevolution for Data Mining of Bayesian Networks

   • Man Leung Wong, Shing Yan Lee, Kwong Sak Leung

   Pages 153-175

8. Knowledge Discovery and Data Mining in Medicine

   • Takumi Ichimura, Shinichi Oeda, Machi Suka, Akira Hara, Kenneth J. Mackin, Katsumi Yoshida

   Pages 177-210

9. Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network

   • Chin-Teng Lin, Her-Chang Pu, Yin-Cheung Lee

   Pages 211-231

10. Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets

    • Shusaku Tsumoto

    Pages 233-252

11. Back Matter

    Pages 253-254

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Data mining and knowledge discovery (DMKD) is a rapidly expanding field in computer science. It has become very important because of an increased demand for methodologies and tools that can help the analysis and understanding of huge amounts of data generated on a daily basis by institutions like hospitals, research laboratories, banks, insurance companies, and retail stores and by Internet users. This explosion is a result of the growing use of electronic media. But what is data mining (DM)? A Web search using the Google search engine retrieves many (really many) definitions of data mining. We include here a few interesting ones. One of the simpler definitions is: “As the term suggests, data mining is the analysis of data to establish relationships and identify patterns” [1]. It focuses on identifying relations in data. Our next example is more elaborate: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis [2].

• Bayesian network
• Internet
• algorithms
• architecture
• calculus
• classification
• clustering
• data mining
• database
• databases
• evolution
• evolutionary algorithm
• knowledge discovery
• networks
• visualization
• data structures
• algorithm analysis and problem complexity

From the reviews:

"This book presents research on some of the recent advances in the field of DMKD, and provides a glimpse into some real-world applications. … book starts with a preface by the editors, including background information as well as an overview of the book’s contents. … The overall layout and the length of the volume … appear to be satisfactory. … postgraduate students and the faculty members in the business intelligence or DMKD fields would find this volume to be a useful addition to their libraries." (C. S. Arora, Computing Reviews, April, 2006)

• Electronics and Communication Sciences Unit, Indian Statistical Institute, India

  Nikhil R. Pal

• KES Center, University of South Australia, Australia

  Lakhmi Jain

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