دانلود کتاب Complex Pattern Mining: New Challenges, Methods and Applications (Studies in Computational Intelligence, 880)

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Preface\nContents\nEfficient Infrequent Pattern Mining Using Negative Itemset Tree\n 1 Introduction\n 2 Related Works\n 3 Preliminaries\n 3.1 Neg-Rep and Negative Itemsets\n 4 Negative Itemset Tree Miner\n 4.1 Negative Itemset Tree and Support Counting\n 4.2 Infrequent Pattern Mining with Termination Nodes Pruning\n 4.3 Infrequent Pattern Mining with 1st-Layer Nodes Pruning\n 5 Experimental Evaluation\n 6 Conclusion and Future Works\n References\nHierarchical Adversarial Training for Multi-domain Adaptive Sentiment Analysis\n 1 Introduction\n 2 Related Work\n 3 Hierarchical Adversarial Sentiment Analysis\n 3.1 Hierarchical RNNs\n 3.2 Semi-supervised Adversarial Training Framework\n 3.3 Sentiment Prediction on an Unknown Domain\n 4 Empirical Analysis\n 4.1 Experiment Setup\n 4.2 Pairwise Domain Adaptation\n 4.3 Multi-domain Adaptation\n 5 Conclusion\n References\nOptimizing C-Index via Gradient Boosting in Medical Survival Analysis\n 1 Introduction and Background\n 2 Our Approach\n 2.1 Derivation\n 2.2 Data Approach\n 3 Datasets\n 4 Methods\n 5 Results\n 6 Summary and Conclusions\n References\nOrder-Preserving Biclustering Based on FCA and Pattern Structures\n 1 Introduction\n 2 Order-Preserving Biclusters\n 3 FCA and Pattern Structures\n 4 Finding Biclusters Using Partition Pattern Structure\n 4.1 Partition Pattern Structure\n 4.2 OP Biclustering Using Partition\n 5 Finding Biclusters Using Sequence Pattern Structure\n 5.1 Sequence Pattern Structure\n 5.2 OP Biclustering Using Sequence\n 6 Experiment\n 7 Conclusion\n References\nA Text-Based Regression Approach to Predict Bug-Fix Time\n 1 Introduction\n 2 Background\n 3 Related Work\n 4 Proposed Model\n 4.1 Data Collection\n 4.2 Pre-processing\n 4.3 Learning and Severity Prediction\n 5 Experiment\n 5.1 Projects Selected\n 5.2 Metrics Used\n 5.3 Results\n 6 Discussion and Conclusion\n 6.1 Threats to Validity\n References\nA Named Entity Recognition Approach for Albanian Using Deep Learning\n 1 Introduction\n 2 Related Works\n 3 Challenges of NER in Albanian Language\n 4 Albanian Corpus Building and Annotation\n 5 System Architecture and Algorithmic Design\n 5.1 Frameworks and Libraries\n 5.2 Preprocessing Module\n 5.3 Neural Network Layer\n 5.4 CRF Layer\n 6 Experimental Analysis and Accuracy Evaluation\n 6.1 Experimental Environment\n 6.2 Experiments\n 6.3 Experimental Results\n 7 Conclusions and Future Works\n References\nA Latitudinal Study on the Use of Sequential and Concurrency Patterns in Deviance Mining\n 1 Introduction\n 2 Deviance Mining\n 3 Sequential Versus Concurrency: Challenges and Benefits\n 4 Experiments\n 4.1 Datasets\n 4.2 Settings\n 4.3 Results\n 5 Discussion\n 6 Conclusion\n References\nEfficient Declarative-Based Process Mining Using an Enhanced Framework\n 1 Introduction\n 2 Background and Related Works\n 3 The WoMan Framework\n 3.1 The WoMan Formalism\n 3.2 WoMan Modules\n 4 Optimization Approaches\n 4.1 Prototyped Process Discovery\n 4.2 Distributed Process Discovery\n 5 Performance Evaluation\n 5.1 Prototype\'s Handling Evaluation\n 5.2 Distributed Approach Evaluation\n 6 Conclusions and Future Directions\n References\nExploiting Pattern Set Dissimilarity for Detecting Changes in Communication Networks\n 1 Introduction\n 2 Related Works\n 3 Preliminaries\n 3.1 Data Representation\n 3.2 Basic Definitions\n 3.3 Pattern Set Dissimilarity\n 3.4 Problem Definition\n 4 The Algorithm\n 5 Experiments\n 5.1 Datasets Description\n 5.2 Influence of the Input Parameters\n 5.3 Comparative Evaluation on Real Networks\n 5.4 Comparative Evaluation on Synthetic Networks\n 5.5 Case Study\n 6 Conclusions\n References\nClassification and Clustering of Emotive Microblogs in Albanian: Two User-Oriented Tasks\n 1 Introduction\n 2 Related Work and Contribution\n 3 Construction of the Sentence-Level Datasets\n 3.1 Data Collection and Assembly\n 3.2 Data Preprocessing\n 4 Sentence-Based Classification\n 4.1 Experimental Setting\n 4.2 Experimental Evaluation\n 5 Keyword Extraction\n 5.1 Cluster-Based Analysis\n 5.2 Keyword Extraction Through Clustering\n 5.3 Methodology\n 5.4 Experimental Results and Discussion\n 6 Conclusions and Future Remarks\n References\nDealing with Class Imbalance in Android Malware Detection by Cascading Clustering and Classification\n 1 Introduction\n 2 Machine Learning Features\n 3 Cascading Clustering and Classification\n 3.1 Classification Algorithm\n 3.2 Clustering Algorithm\n 3.3 Cascading Clustering and Classification\n 4 Experimental Analysis\n 4.1 Data\n 4.2 Experimental Methodology\n 4.3 Compared Algorithms\n 4.4 Results and Discussion\n 5 Conclusion\n References\nApplying Analytics to Artist Provided Text to Model Prices of Fine Art\n 1 Introduction\n 2 Methods\n 2.1 Dataset Acquisition and Design\n 2.2 Product Description\n 2.3 Social Media and Sales\n 2.4 Determining Text Similarity Using Vectors\n 2.5 Sentiment Analysis\n 3 Results\n 3.1 Base Features\n 3.2 Social Media Presence\n 3.3 Word Count Results\n 3.4 Document Vector Clusters\n 3.5 Results with Sentiment\n 3.6 Combined Features\n 4 Conclusions\n References\nApproximate Query Answering over Incomplete Data\n 1 Introduction\n 2 Background\n 3 System Overview\n 4 Experimental Evaluation of Approximation Algorithms\n 5 Conclusion\n References\nA Machine Learning Approach for Walker Identification Using Smartphone Sensors\n 1 Introduction\n 2 Background on Decision Tree Classification\n 2.1 Random Forests\n 3 Related Work on Learning Algorithms for Mobile Sensors Data\n 4 Methodology\n 4.1 Features Model\n 4.2 Classification Approach\n 4.3 The Adopted Classifiers\n 5 Evaluation\n 5.1 Description of the Experiments\n 5.2 Evaluation Setting\n 6 Results and Discussion\n 6.1 D1 Dataset Results\n 6.2 D2 Dataset Results\n 7 Conclusions\n References\nAuthor Index