Pacific-Asia Conference on Knowledge Discovery and Data Mining
摘要截稿:
全文截稿: 2018-10-10
开会时间: 2019-04-14
会议难度:
CCF分类: C类
会议地点: Macau, China
Overview
The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. It provides an international forum for researchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications.
PAKDD 2019 welcomes high-quality, original and previously unpublished submissions in the theory, practice, and applications on all aspects of knowledge discovery and data mining.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:
-Theoretic foundations of KDD
-Deep learning theory and applications in KDD
-Novel models and algorithms
-Statistical methods and graphical models for data mining
-Anomaly detection and analytics
-Association analysis
-Clustering
-Classification
-Data pre-processing
-Feature extraction and selection
-Post-processing including quality assessment and validation
-Mining heterogeneous/multi-source data
-Mining sequential data
-Mining spatial and temporal data
-Mining unstructured and semi-structured data
-Mining graph and network data
-Mining social networks
-Mining high dimensional data
-Mining uncertain data
-Mining imbalanced data
-Mining dynamic/streaming data
-Mining behavioral data
-Mining multi-media data
-Mining scientific data
-Privacy preserving data mining
-Fraud and risk analysis
-Security and intrusion detection
-Visual data mining
-Interactive and online mining
-Ubiquitous knowledge discovery and agent-based data mining
-Integration of data warehousing, OLAP, and data mining
-Parallel, distributed, and cloud-based high-performance data mining
-Opinion mining and sentiment analysis
-Human, domain, organizational, and social factors in data mining
-Applications to healthcare, bioinformatics, computational chemistry, finance, eco-informatics, marketing, gaming, cyber-security, and industry-related problems