ADVANCES IN DATA MINING AND MODELING
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ADVANCES IN DATA MINING AND MODELING
Hong Kong 27 - 28 June 2002
edited by Wai-Ki Ching & Michael Kwok-Po Ng (University of Hong Kong, China)
Data mining and data modeling are hot topics and are under fast development. Because of their wide applications and rich research contents, many practitioners and academics are attracted to work in these areas. With a view to promoting communication and collaboration among the practitioners and researchers in Hong Kong, a workshop on data mining and modeling was held in June 2002. Prof Ngaiming Mok, Director of the Institute of Mathematical Research, The University of Hong Kong, and Prof Tze Leung Lai (Stanford University), C V Starr Professor of the University of Hong Kong, initiated the workshop.
This book contains selected papers presented at the workshop. The papers fall into two main categories: data mining and data modeling. Data mining papers deal with pattern discovery, clustering algorithms, classification and practical applications in the stock market. Data modeling papers treat neural network models, time series models, statistical models and practical applications.
Contents:
- Data Mining:
- Algorithms for Mining Frequent
Sequences (B Kao & M-H Zhang)
- Cluster Analysis Using Unidimensional Scaling (P-L Leung et al.)
- From Associated Implication Networks to Intermarket Analysis (P C-W Tse & J-M Liu)
- Automating Technical Analysis (P L-H Yu et al.)
- Data Modeling:
- Learning Sunspot Series Dynamics by Recurrent Neural Networks (L-K Li)
- Bond Risk and Return in the SSE (L-Z Fan)
- Mining Loyal Customers: A Practical Use of the Repeat Buying Theory (H-P Lo et al.)
- and other papers
View Full Text (8,615 KB)
Readership: Graduate students, researchers and practitioners in data
mining, data modeling, engineering and computer science.
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196pp
Pub. date: Apr 2003
eISBN 981-270-495-7
Price: US$100
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