Pacific-Asia Conference on Knowledge Discovery and Data Mining
摘要截稿: 2019-11-18
全文截稿: 2019-11-25
开会时间: 2020-05-11
会议难度:
CCF分类: C类
会议地点: Singapore
Overview
PAKDD 2020 welcomes high-quality, original and previously unpublished submissions in the theory, practice, and applications on all aspects of knowledge discovery and data mining. Topics of relevance for the conference include, but not limited to, the following:
Anomaly detection and analytics
Association analysis
Classification
Clustering
Data pre-processing
Deep learning theory and applications in KDD
Explainable machine learning
Factor and tensor analysis
Feature extraction and selection
Fraud and risk analysis
Human, domain, organizational, and social factors in data mining
Integration of data warehousing, OLAP, and data mining
Interactive and online mining
Mining behavioral data
Mining dynamic/streaming data
Mining graph and network data
Mining heterogeneous/multi-source data
Mining high dimensional data
Mining imbalanced data
Mining multi-media data
Mining scientific data
Mining sequential data
Mining social networks
Mining spatial and temporal data
Mining uncertain data
Mining unstructured and semi-structured data
Novel models and algorithms
Opinion mining and sentiment analysis
Parallel, distributed, and cloud-based high-performance data mining
Post-processing including quality assessment and validation
Privacy preserving data mining
Recommender systems
Representation learning and embedding
Security and intrusion detection
Statistical methods and graphical models for data mining
Supervised learning
Theoretic foundations of KDD
Ubiquitous knowledge discovery and agent-based data mining
Unsupervised learning
Visual data mining
Applications to healthcare, bioinformatics, computational chemistry, finance, eco-informatics, marketing, gaming, cyber-security, and industry-related problems