Advanced Data Mining and Applications
Springer International Publishing (Verlag)
978-3-031-46670-0 (ISBN)
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.
Pharmaceutical Data Analysis.- Drug-target interaction prediction based on drug subgraph fingerprint extraction strategy and subgraph attention mechanism.- Soft Prompt Transfer for Zero-Shot and Few-Shot Learning in EHR Understanding.- Graph Convolution Synthetic Transformer for Chronic Kidney Disease Onset Prediction.- MTFL: Multi-task feature learning with joint correlation structure learning for Alzheimer's disease cognitive performance prediction.- Multi-Level Transformer for Cancer Outcome Prediction in Large-Scale Claims Data.- Individual Functional Network Abnormalities Mapping via Graph Representation-based Neural Architecture Search.- A novel application of a mutual information measure for analysing temporal changes in healthcare network graphs.- Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation.- Text Classification.- ParaNet:Parallel Networks with Pre-trained Models for Text Classification.- Open Text Classification Based on Dynamic Boundary Balance.- A Prompt Tuning Method for Chinese Medical Text Classification.- TabMentor: Detect Errors on Tabular Data with Noisy Labels.- Label-aware Hierarchical Contrastive Domain Adaptation for Cross-network Node Classification.- Semi-supervised classification based on Graph Convolution Encoder Representations from BERT.- Global Balanced Text Classification for Stable Disease Diagnosis.- Graph.- Dominance Maximization in Uncertain Graphs.- LAGCL: Towards Stable and Automated Graph Contrastive Learning.- Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model.- Common-Truss-based Community Search on Multilayer Graphs.- Learning To Predict Shortest Path Distance.- Efficient Regular Path Query Evaluation with Structural Path Constraints.EnSpeciVAT: Enhanced SpeciVAT for Cluster Tendency Identification in Graphs.- Pessimistic Adversarially Regularized Learning for Graph Embedding.- M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning.
| Erscheinungsdatum | 07.11.2023 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XXI, 370 p. 136 illus., 86 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 592 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Schlagworte | Artificial Intelligence • Big Data • Computational Linguistics • Computer Networks • Computer systems • computer vision • disaster prediction • DNA sequencing, genomics, and biometrics • E-commerce and Web services • grid computing • Health Informatics • High performance data mining algorithms • Image interpretations • Industry: Innovative industrial advancements • remote monitoring • Sequence processing and analysis • Spatial Data Mining • Text, video, multimedia data mining • Web mining • Web of Things |
| ISBN-10 | 3-031-46670-5 / 3031466705 |
| ISBN-13 | 978-3-031-46670-0 / 9783031466700 |
| Zustand | Neuware |
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