Data Mining: Principles, Applications and Emerging Challenges

Harold L. Capri (Editor)

Series: Computer Science, Technology and Applications
BISAC: COM014000



Volume 10

Issue 1

Volume 2

Volume 3

Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick


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Data mining is an area of research where appropriate methodological research and technical means are experienced to produce useful knowledge from different types of data. Data mining techniques use a broad family of computationally intensive methods that include decision trees, neural networks, rule induction, machine learning and graphic visualization. This book discusses the principles, applications and emerging challenges of data mining.
(Imprint: Nova)


Chapter 1 - Transit Passenger Origin Inference Using Smart Card Data and GPS Data (pp. 1-32)
Xiaolei Ma, Ph.D. and Yinhai Wang, Ph.D. (School of Transportation Science and Engineering, Beihang University, Beijing, China, and others)

Chapter 2 - Knowledge Extraction from an Automated Formative Evaluation Based on Odala Approach Using the Weka Tool? (pp. 33-52)
Farida Bouarab-Dahmani and Razika Tahi (The Computer science Department, FGEI faculty, University of Tizi-Ouzou, Tizi-Ouzou, Algeria, and others)

Chapter 3 - Modeling Nations’ Failure via Data Mining Techniques (pp. 53-88)
Mohamed M. Mostafa, Ph.D. (Gulf University for Science and Technology, Kuwait)

Chapter 4 - An Evolutionary Self-Adaptive Algorithm for Mining Association Rules (pp. 89-124)
Jośe María Luna, Alberto Cano and Sebastián Ventura (Dept. of Computer Science and Numerical Analysis, University of Cordoba, and others)


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