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Programme Chairs' Introduction; Acknowledgements/Committees; Technical Executive Programme Committee; Applications Executive Programme Committee; Technical Programme Committee; Application Programme Committee; Contents; Research and Development in Intelligent Systems XXXIII; Harnessing Background Knowledge for E-Learning Recommendation; 1 Introduction; 2 Related Work; 3 Background Knowledge Representation; 3.1 Knowledge Sources; 3.2 Generating Potential Domain Concept Labels; 3.3 Verifying Concept Labels Using Domain Lexicon; 3.4 Domain Concept Generation
4 Representation Using Background Knowledge4.1 The ConceptBased Approach; 4.2 The Hybrid Approach; 5 Evaluation; 5.1 Evaluation Method; 5.2 Results and Discussion; 6 Conclusions; References; Knowledge Discovery and Data Mining; Category-Driven Association Rule Mining; 1 Introduction; 2 Mining Association Rules Revisited; 3 Category-Augmented Knowledge; 3.1 Graphical Representation; 4 Category-Augmented Rule-Association Mining Using Background Knowledge; 4.1 Category-Derived Constraints; 4.2 Score Evaluation; 4.3 The Algorithm; 4.4 Time Complexity
5 Experimental Evaluation Using Medical Records6 Summary and Conclusions; References; A Comparative Study of SAT-Based Itemsets Mining; 1 Introduction; 2 Background; 3 Frequent Itemset Mining; 4 Encoding of Frequent Itemset Mining Using Constraint; 5 Enumerating all Models of CNF Formulae; 6 Experimental Validation; 6.1 Experiments on Frequent Closed Itemset Mining; 6.2 Experiments on Top-k; 7 Conclusion; References; Mining Frequent Movement Patterns in Large Networks: A Parallel Approach Using Shapes; 1 Introduction; 2 Literature Review; 3 Formalism; 4 Movement Pattern Mining
4.1 The Shape Based Movement Pattern (ShaMP) Algorithm4.2 The Apriori Based Movement Pattern (AMP) Algorithm; 5 Experiments and Evaluation; 5.1 Data Sets; 5.2 Support Threshold; 5.3 Number of FETs; 5.4 Number of Shapes; 5.5 Distributed ShaMP; 6 Conclusion and Future Work; References; Sentiment Analysis and Recommendation; Emotion-Corpus Guided Lexicons for Sentiment Analysis on Twitter; 1 Introduction; 2 Related Work; 2.1 Lexicons for Sentiment Analysis; 2.2 Emotion Theories; 3 Emotion-Aware Models for Sentiment Analysis; 3.1 Emotion Corpus-EmoSentilex; 3.2 Emotion Corpus-Sentilex
4 Mixture Model for Lexicon Generation5 Evaluation; 5.1 Evaluation Tasks; 5.2 Datasets; 5.3 Baselines and Metrics; 5.4 Results and Analysis; 6 Conclusions; References; Context-Aware Sentiment Detection from Ratings; 1 Introduction; 2 Creating a Domain-Specific Sentiment Lexicon; 2.1 Word-Level Sentiment Scoring; 2.2 Dealing with Negation; 3 Creating a Context-Sensitive Sentiment Lexicon; 4 Evaluation; 4.1 Datasets; 4.2 Domain-Specific Lexicon Coverage; 4.3 Sentiment Polarity Prediction; 5 Conclusion; References; Recommending with Higher-Order Factorization Machines; 1 Introduction
4 Representation Using Background Knowledge4.1 The ConceptBased Approach; 4.2 The Hybrid Approach; 5 Evaluation; 5.1 Evaluation Method; 5.2 Results and Discussion; 6 Conclusions; References; Knowledge Discovery and Data Mining; Category-Driven Association Rule Mining; 1 Introduction; 2 Mining Association Rules Revisited; 3 Category-Augmented Knowledge; 3.1 Graphical Representation; 4 Category-Augmented Rule-Association Mining Using Background Knowledge; 4.1 Category-Derived Constraints; 4.2 Score Evaluation; 4.3 The Algorithm; 4.4 Time Complexity
5 Experimental Evaluation Using Medical Records6 Summary and Conclusions; References; A Comparative Study of SAT-Based Itemsets Mining; 1 Introduction; 2 Background; 3 Frequent Itemset Mining; 4 Encoding of Frequent Itemset Mining Using Constraint; 5 Enumerating all Models of CNF Formulae; 6 Experimental Validation; 6.1 Experiments on Frequent Closed Itemset Mining; 6.2 Experiments on Top-k; 7 Conclusion; References; Mining Frequent Movement Patterns in Large Networks: A Parallel Approach Using Shapes; 1 Introduction; 2 Literature Review; 3 Formalism; 4 Movement Pattern Mining
4.1 The Shape Based Movement Pattern (ShaMP) Algorithm4.2 The Apriori Based Movement Pattern (AMP) Algorithm; 5 Experiments and Evaluation; 5.1 Data Sets; 5.2 Support Threshold; 5.3 Number of FETs; 5.4 Number of Shapes; 5.5 Distributed ShaMP; 6 Conclusion and Future Work; References; Sentiment Analysis and Recommendation; Emotion-Corpus Guided Lexicons for Sentiment Analysis on Twitter; 1 Introduction; 2 Related Work; 2.1 Lexicons for Sentiment Analysis; 2.2 Emotion Theories; 3 Emotion-Aware Models for Sentiment Analysis; 3.1 Emotion Corpus-EmoSentilex; 3.2 Emotion Corpus-Sentilex
4 Mixture Model for Lexicon Generation5 Evaluation; 5.1 Evaluation Tasks; 5.2 Datasets; 5.3 Baselines and Metrics; 5.4 Results and Analysis; 6 Conclusions; References; Context-Aware Sentiment Detection from Ratings; 1 Introduction; 2 Creating a Domain-Specific Sentiment Lexicon; 2.1 Word-Level Sentiment Scoring; 2.2 Dealing with Negation; 3 Creating a Context-Sensitive Sentiment Lexicon; 4 Evaluation; 4.1 Datasets; 4.2 Domain-Specific Lexicon Coverage; 4.3 Sentiment Polarity Prediction; 5 Conclusion; References; Recommending with Higher-Order Factorization Machines; 1 Introduction