Advanced 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 Yu

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (430 pages)
• Disciplina: 006.3; 006.312
• Soggetto topico: Artificial intelligence; Computers; Computer engineering; Computer networks; Social sciences - Data processing; Artificial Intelligence; Computing Milieux; Computer Engineering and Networks; Computer Application in Social and Behavioral Sciences
• Persona (resp. second.): LiBohan
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Advanced Data Mining and Applications
• ISBN: 3-030-95408-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910523766303321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 13088