Advanced Data Mining and Applications

19th International Conference, ADMA 2023, Shenyang, China, August 21–23, 2023, Proceedings, Part I

AvXiaochun Yang,Heru Suhartanto

Häftad, Engelska, 2023

Del i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023.The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.

Produktinformation

Utgivningsdatum:2023-11-05
Mått:155 x 235 x 46 mm
Vikt:1 276 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:833
Förlag:Springer International Publishing AG
ISBN:9783031466601

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT

Innehållsförteckning

Time Series.- An Adaptive Data-Driven Imputation Model for Incomplete Event Series.- From Time Series to Multi-Modality: Classifying Multivariate Time Series via Both 1D and 2D Representations.- Exploring the Effectiveness of Positional Embedding on Transformer-based Architectures for Multivariate Time Series Classification.- Modeling of Repeated Measures for Time-to-Event Prediction.- A Method for Identifying the Timeliness of Manufacturing Data Based on Weighted Timeliness Graph.- STAD: Multivariate Time Series Anomaly Detection Based on Spatio-temporal Relationship.- Recommendation I.- Refined Node Type Graph Convolutional Network for Recommendation.- Multi-level Noise Filtering and Preference Propagation Enhanced Knowledge Graph Recommendation.- Enhancing Knowledge-aware Recommendation with Contrastive Learning.- Knowledge-Rich Influence Propagation Recommendation Algorithm Based on Graph Attention Networks.- A Novel Variational Autoencoder with Multi-Position Latent Self-Attention and Actor-Critic for Recommendation.- Fair Re-ranking Recommendation Based on Debiased Multi-Graph Representations.- Information Extraction.- FastNER: Speeding Up Inferences for Named Entity Recognition Tasks.- CPMFA: A Character Pair-Based Method for Chinese Nested Named Entity Recognition.- STMC-GCN: A Span Tagging Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction.- Exploring the Design Space of Unsupervised Blocking with Pre-trained Language Models in Entity Resolution.- Joint Modeling of Local and Global Semantics for Contrastive Entity Disambiguation.- Fine-grained Review Analysis using BERT with Attention: A Categorical and Rating-based Approach.- Emotional Analysis.- Discovery of Emotion Implicit Causes in Products based on Commonsense Reasoning.- Multi-modal Multi-emotion Emotional Support Conversation.- Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations.- Generating Enlightened Suggestions based on Mental State Evolution for Emotional Support Conversation.- Deep One-Class Fine-Tuning for Imbalanced Short Text Classification in Transfer Learning.- EmoKnow: Emotion- and Knowledge-oriented Model for COVID-19 Fake News Detection.- Popular Songs: The Sentiment Surrounding the Conversation.- Market Sentiment Analysis based on Social Media and Trading Volume for Asset Price Movement Prediction.- Data Mining.- Efficient mining of high utility co-location patterns based on a query strategy.- Point-level Label-free Segmentation Framework for 3D Point Cloud Semantic Mining.- CD-BNN: Causal Discovery with Bayesian Neural Network.- A Preference-based Indicator Selection Hyper-heuristic for Optimization Problems.- An Elastic Scalable Grouping for Stateful Operators in Stream Computing Systems.- Incremental natural gradient boosting for probabilistic regression.- Discovering Skyline Periodic Itemset Patterns in Transaction Sequences.- Double-optimized CS-BP Anomaly Prediction for Control Operation Data.- Bridging the Interpretability Gap in Coupled Neural Dynamical Models.- Multidimensional Adaptative kNN Over Tracking Outliers (Makoto).- Traffic.- MANet: An End-to-End Multiple Attention Network for Extracting Roads around EHV Transmission Lines from High-Resolution Remote Sensing Images.- Deep Reinforcement Learning for Solving the Trip Planning Query.- MDCN: Multi-Scale Dilated Convolutional Enhanced Residual Network for Traffic Sign Detection.- Identifying Critical Congested Roads based on Traffic Flow-Aware Road Network Embedding.- A Cross-Region-based Framework for Supporting Car-Sharing.- Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting.- Transformer Based Driving Behavior Safety Prediction For New Energy Vehicles.- Graph Convolution Recurrent Denoising Diffusion Model for Multivariate Probabilistic Temporal Forecasting.- A Bottom-Up Sampling Strategy for Reconstructing Geospatial Data from Ultra Sparse Inputs.- Recommendation II.- Feature Representation Enhancing by Context Sensitive Information in CTR Prediction.- ProtoMix: Learnable Data Augmentation on Few-shot Features with Vector Quantization in CTR Prediction.- When Alignment Makes a Difference: A Content-Based Variational Model for Cold-Start CTR Prediction.- Dual-Ganularity Contrastive Learning for Session-based Recommendation.- Efficient Graph Collaborative Filtering with Multi-layer Output-enhanced Contrastive Learning.- Influence Maximization with Tag Revisited: Exploiting the Bi-Submodularity of the Tag-Based Influence Function.- Multi-Interest Aware Graph Convolution Network for Social Recommendation.- Enhancing MultimediaRecommendation through Item-Item Semantic Denoising and Global Preference Awareness.- Resident-based Store Recommendation Model for Community Commercial Planning.c