01106nam0 22003013i 450 UBO230730320231121125858.020190912d1958 ||||0itac50 bagerdez01i xxxe z01nEuropäischer Humanismus: ErasmusJohan HuizingaHamburgRowohlt1958195 p., [4] p. di tav.ill.19 cmRowohlts deutsche Enzyklopädie78001MIL01227802001 Rowohlts deutsche Enzyklopädie78Erasmo : da RotterdamFIRRMLC001831I199.492FILOSOFIA OCCIDENTALE MODERNA. OLANDA22Huizinga, JohanCFIV016495070292479ITIT-0120190912IT-FR0017 Biblioteca umanistica Giorgio ApreaFR0017 NUBO2307303Biblioteca umanistica Giorgio Aprea 52DGA F 1 52SBA0000253165 VMB RS A 2019091220190912 52Europäischer Humanismus: Erasmus3640468UNICAS12542nam 22007455 450 991052376630332120251113195047.03-030-95408-010.1007/978-3-030-95408-6(MiAaPQ)EBC6876625(Au-PeEL)EBL6876625(CKB)21028240700041(PPN)264961749(OCoLC)1294932388(DE-He213)978-3-030-95408-6(EXLCZ)992102824070004120220127d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAdvanced Data Mining and Applications 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, Proceedings, Part II /edited by Bohan Li, Lin Yue, Jing Jiang, Weitong Chen, Xue Li, Guodong Long, Fei Fang, Han Yu1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (430 pages)Lecture Notes in Artificial Intelligence,2945-9141 ;13088Print version: Li, Bohan Advanced Data Mining and Applications Cham : Springer International Publishing AG,c2022 9783030954079 Includes bibliographical references and index.Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Pattern Mining -- SMIM Framework to Generalize High-Utility Itemset Mining -- 1 Introduction -- 2 Problem Statement -- 3 Related Work -- 4 Examples of SMIM -- 5 Algorithmic Framework for SMIM -- 5.1 Projection-Based Algorithms -- 5.2 Tree-Based Algorithms -- 5.3 SM-Miner Algorithm -- 5.4 Empirical Observations -- References -- TKQ: Top-K Quantitative High Utility Itemset Mining -- 1 Introduction -- 2 Related Work -- 3 Preliminaries and Problem Definition -- 4 The TKQ Algorithm -- 5 Experiments -- 6 Conclusion -- References -- OPECUR: An Enhanced Clustering-Based Model for Discovering Unexpected Rules -- 1 Introduction -- 2 Background -- 3 Related Work -- 4 Proposed Method: OPECUR Model -- 4.1 Generating Association Rule -- 4.2 Clustering Algorithm -- 5 Experimental Evaluation -- 5.1 Experimental Setup -- 5.2 Experiment 1: Execution Time Comparison -- 5.3 Experiment 2: Clustering Process Comparison -- 5.4 Experiment 3: Evaluation of Unexpected Rules -- 6 Conclusion -- References -- Tourists Profiling by Interest Analysis -- 1 Introduction -- 2 State of the Art -- 3 Tourism Movement's Data Model -- 3.1 Sequences Dataset -- 3.2 Sequential Rule Mining -- 3.3 Measure of Interest -- 3.4 Graph Movement Model -- 4 Community Detection -- 4.1 Mainstream Nodes -- 4.2 Spheres of Influence -- 4.3 Similarity Measure -- 4.4 Profiling -- 5 Experiments -- 5.1 Measure of Interest -- 5.2 Mainstream Monuments -- 5.3 Sphere of Influence -- 5.4 Clustering Analysis -- 5.5 Discussions -- 6 Conclusion -- References -- Extracting High Profit Sequential Feature Groups of Products Using High Utility Sequential Pattern Mining -- 1 Introduction -- 1.1 Opinion Mining (OM) and Sentiment Analysis (SA) -- 1.2 High Utility Sequential Pattern Mining (HUSPM) -- 1.3 Problem Definition -- 1.4 Contributions.2 Related Work -- 3 Proposed High Profit Sequential Feature Groups Based on High Utility Sequences (HPSFG_HUS) System -- 4 Experimental Evaluation -- 4.1 Dataset and Implementation Details -- 4.2 Comparison Analysis of HPSFG_HUS System -- 5 Conclusion and Future Work -- References -- Game Achievement Analysis: Process Mining Approach -- 1 Introduction -- 2 Background -- 2.1 Process Mining -- 2.2 Achievements -- 3 Related Work -- 4 Data Preparation -- 4.1 Steam Achievements Extraction -- 4.2 Conversion to Event Log -- 4.3 Game Categorization -- 4.4 Selected Games -- 4.5 Data Filtering -- 5 Analysis of Game Achievements -- 5.1 Typical Playthrough -- 5.2 Comparing Player Behaviour -- 5.3 Game Level Analysis -- 5.4 Noise Detection -- 6 Discussion -- 7 Conclusion -- References -- A Fast and Accurate Approach for Inferencing Social Relationships Among IoT Objects -- 1 Introduction -- 2 Problem Formulation and Basic Definitions -- 2.1 Basic Definitions -- 2.2 Problem Statement -- 3 SociRence: The Proposed Approach -- 4 Experiments -- 4.1 Datasets Description -- 4.2 Baselines -- 4.3 Performance Evaluation -- 4.4 Effect of ``distance'' and ``duration'' on the Social Structure -- 5 Related Works -- 6 Conclusion and Future Work -- References -- Graph Mining -- A Local Seeding Algorithm for Community Detection in Dynamic Networks -- 1 Introduction -- 2 Notations -- 3 Static Seeding by Local Strategy -- 3.1 Local Seeding Algorithm -- 3.2 Local Centrality Measuring -- 3.3 Hybrid Local Centrality Measuring -- 4 Dynamic Local Seeding -- 4.1 Updating Local Centrality -- 4.2 Dynamic Local Seeding Algorithm -- 5 Experiments -- 5.1 Datasets and Evaluation Metrics -- 5.2 Experimental Results on Static Networks -- 5.3 Experimental Results on Dynamic Networks -- 6 Conclusions -- References -- Clique Percolation Method: Memory Efficient Almost Exact Communities.1 Introduction -- 2 Related Work -- 3 Algorithm -- 3.1 Union-Find Structure -- 3.2 Exact cpm Algorithm -- 3.3 Memory Efficient cpm Approximation -- 4 Analysis -- 5 Experimental Evaluation -- 5.1 Comparison with the State of the Art -- 5.2 Memory Gain of the cpmz Algorithm -- 5.3 cpmz Communities Are Very Close to cpm Communities -- 6 Conclusion and Discussions -- References -- Knowledge Graph Embedding Based on Quaternion Transformation and Convolutional Neural Network -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Quaternion Space -- 3.2 Constructing Quaternions of Entities and Relations -- 3.3 Convolutional Network Designed -- 3.4 Definition of Loss Function -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation Protocol -- 4.3 Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Text-Enhanced Knowledge Graph Representation Model in Hyperbolic Space -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Entity Annotation -- 3.2 Textual Context Embedding -- 3.3 Hyperbolic Space Modeling -- 3.4 Representation Training -- 4 Experiment -- 4.1 DateSet -- 4.2 Evaluation Protocol -- 4.3 Link Prediction -- 5 Conclusion -- References -- Relations Reconstruction in a Knowledge Graph of a Socioeconomic System -- 1 Introduction -- 2 Problem Statement -- 3 Related Work -- 4 Dataset and Preprocessing -- 4.1 Ontology Construction -- 4.2 Entity Matching -- 5 Experiments -- 6 Results -- 7 Conclusion -- References -- A Knowledge Enabled Data Management Method Towards Intelligent Police Applications -- 1 Introduction -- 2 Related Concepts and Technologies -- 2.1 Knowledge Graph -- 2.2 Ontology -- 3 SmartHotel Overview -- 3.1 ShDO -- 3.2 Knowledge Extraction -- 3.3 Knowledge Fusion -- 3.4 Reasoning Rules -- 4 Experiments -- 4.1 Purpose -- 4.2 Datasets -- 4.3 Experimental Results and Analysis -- 5 Related Work -- 6 Conclusion -- References.Text Mining -- Sparse Generalized Dirichlet Prior Based Bayesian Multinomial Estimation -- 1 Introduction -- 2 Preliminary Definitions -- 3 The Proposed Approach -- 4 Experimental Results -- 4.1 Emotion Prediction in Poetry Context -- 4.2 Modeling the Flow of Emotions Related to Natural Disasters -- 5 Conclusion -- References -- I Know You Better: User Profile Aware Personalized Dialogue Generation -- 1 Introduction -- 2 Related Work -- 2.1 Meta-learning -- 2.2 Personalized Dialogue Generation -- 3 Personalized Dialogue Generation -- 3.1 Profile Aware Dialogue Generation -- 3.2 Sparse Profile Dialogue Generation -- 4 Experiment -- 4.1 Dataset -- 4.2 Baselines -- 4.3 Implementation Details -- 4.4 Evaluation Metrics -- 4.5 Evaluation Results -- 4.6 Case Study -- 5 Conclusion -- References -- Label-Value Extraction from Documents Using Co-SSL Framework -- 1 Introduction -- 2 Related Work -- 3 Proposed Co-SSL Label-Value Extraction Framework -- 3.1 Candidate Extraction -- 3.2 Candidate Context Extraction -- 3.3 Data Augmentation -- 3.4 Semi-supervised Learning for the Co-SSL Framework -- 3.5 Implementation Details -- 4 Datasets and Protocols -- 5 Results and Analysis -- 6 Conclusion and Future Work -- References -- Entity Relations Based Pointer-Generator Network for Abstractive Text Summarization -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Pointer-Generator Network -- 3.2 Graph Attention Network -- 4 The Proposed Model -- 4.1 Informative OpenIE Triples Selection Algorithm -- 4.2 Entity Relations Graph Attention Network -- 4.3 Entity-Focused Attention Method -- 5 Experiments -- 5.1 Datasets -- 5.2 Data Preprocessing -- 5.3 Impementation Details -- 5.4 Quantitative Results -- 5.5 Ablation Studies -- 6 Conclusion -- References -- Linguistic Dependency Guided Graph Convolutional Networks for Named Entity Recognition -- 1 Introduction.2 Related Work -- 3 Model -- 3.1 BiLSTM-CRF -- 3.2 GCN -- 3.3 SDP-BiLSTM-GCN-CRF -- 4 Experiment -- 4.1 Datasets -- 4.2 Experiment Setup -- 4.3 Results -- 5 Analysis -- 6 Conclusion -- References -- Multimedia and Time Series Data Mining -- CS-Siam: Siamese-Type Network Tracking Method with Added Cluster Segmentation -- 1 Introduction -- 2 Related Works -- 2.1 Siamese Network Based Trackers -- 2.2 Image Segmentation Based on Clustering -- 3 CS-Siam -- 3.1 Clustering Image Segmentation and Input -- 3.2 Siamese Network Structure -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Dataset -- 4.3 Comparison Model -- 4.4 Evaluation Metrics -- 4.5 Result on OTB2015 -- 4.6 Result on VOT2018 -- 5 Conclusion -- References -- On Group Theory and Interpretable Time Series Primitives -- 1 Introduction -- 2 Preliminaries -- 3 Extracting Shapeoids in SAX -- 3.1 Lexical Shapeoids -- 4 Group Theory and Shapeoid Extraction -- 5 Conclusion and Discussion -- References -- Target Detection in Infrared Image of Transmission Line Based on Faster-RCNN -- 1 Introduction -- 2 Target Detection Algorithm Based on Infrared Image -- 2.1 Transmission Line Target Detection Algorithm -- 2.2 Faster-RCNN Structure Parameter Selection Optimization -- 3 Experiment -- 3.1 Dataset Establishment -- 3.2 Analysis of Results -- 3.3 Experiment -- 4 Conclusion -- References -- Automatic Quality Improvement of Data on the Evolution of 2D Regions -- 1 Introduction -- 2 Data Quality Improvement -- 2.1 Creating Quadtree-Based Time Series -- 2.2 Identifying and Removing Inconsistent Data -- 3 Experimental Evaluation -- 3.1 Datasets and Tools -- 3.2 Quadtree Generation -- 3.3 Building the Time Series -- 3.4 Consistent Data Selection -- 4 Related Work -- 5 Conclusions and Future Work -- References -- Cross-modal Data Linkage for Common Entity Identification -- 1 Introduction.2 Related Work.This book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.* The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part II named: Pattern mining; Graph mining; Text mining; Multimedia and time series data mining; and Classification, clustering and recommendation. * The conference was originally planned for December 2021, but was postponed to 2022.Lecture Notes in Artificial Intelligence,2945-9141 ;13088Artificial intelligenceComputersComputer engineeringComputer networksSocial sciencesData processingArtificial IntelligenceComputing MilieuxComputer Engineering and NetworksComputer Application in Social and Behavioral SciencesArtificial intelligence.Computers.Computer engineering.Computer networks.Social sciencesData processing.Artificial Intelligence.Computing Milieux.Computer Engineering and Networks.Computer Application in Social and Behavioral Sciences.006.3006.312Li BohanMiAaPQMiAaPQMiAaPQBOOK9910523766303321Advanced Data Mining and Applications2982700UNINA