Data Mining in Crystallography
Humans have been “manually” extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automatic approaches. Early methods of identifying patterns in data include Bayes’ theorem (1700s) and Regressio
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Editors
C. Romero University of Cordoba, Spain S. Ventura University of Cordoba, Spain
W
1 1 PRESS
Southampton, Boston
Contents
Preface
xv
Biography
xix
Parti An introduction to e-learning systems, data mining and their interactions Chapter 1 Web-based educational hypermedia P. De Bra 1 2 3 4
Introduction Adaptive (educational) hypermedia The AHAM reference architecture A general-purpose adaptive web-based platform 4.1 Overall architecture of AHA! 4.2 The AHA! authoring tools 5 Questions, quizzes and tasks 6 Adapting to learning styles 7 Conclusions
Chapter 2 Web mining for self-directed e-learning P. Desikan, C. DeLong, K. Beemanapalli, A. Bose & J. Srivastava 1 Introduction 2 Why self-directed e-learning? 3 Web-based self-directed e-learning applications 3.1 Google Scholar 3.2 Westlaw 3.3 CiteSeer 3.4 LexisNexis 3.5 Knowledge management systems 3.6 Dr. Spock's child care
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4 Gaps in existing technology 4.1 Lack of community collaboration 4.2 Time management 4.3 Not self-improving 4.4 Implicit relationships not mapped 5 Web mining 5.1 Web mining taxonomy 5.1.1 Web content mining 5.1.2 Web structure mining 5.1.3 Web usage mining 5.2 Web mining research: state of the art 5.3 Web mining applicable to e-learning 6 Future directions of research 6.1 Usage rules 6.2 Keyword clustering: the conceptual thesaurus 6.3 Recommendation mining 6.4 Smart results: model of relevance 6.5 Intelligent knowledge management systems 7 Conclusion Chapter 3 Data mining for the analysis of content interaction in web-based learning and training systems C. Pahl 1 Introduction 2 Interaction and behavior.. 2.1 Learning and training interaction 2.2 Implementing interaction 2.3 An abstract model of content interaction 2.4 The interactive database learning environment 3 Data and web usage mining 3.1 Web usage mining in the educational context 3.2 Data and web mining techniques 3.3 Education-specific web usage mining 4 Session statistics ,. 5 Session classification 6 Behavioral patterns 7 Time series 8 Conclusions Chapter 4 On using data mining for browsing log analysis in learning environments F. Wang 1 Introduction
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2 Data mining 2.1 Association mining 2.2 Clustering 2.3 Web usage mining 3 Recommendation systems 3.1 Content-based filtering systems 3.2 Collaborative filtering systems 3.3 Recommendation systems based on association rules mining technologies 4 The research framework 5 Construction of browsing content structure 5.1 Data pre-processing 5.2 Model definition and construction 5.3 Model application 5.4 Summary statements 6 Personalized recommendation based on association mining 6.1 Model definition and construction 6.1.1 User browsing similarity in time-framed navigation sessions 6.1.2 The HBM clustering algorithm 6.1.3 Mining association rules 6.2 Model application 6.2.1 User classification 6.2.2 The window-sliding method 6.2.3 The maximal-matching m
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