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Due to the unexpected COVID-19 epidemic, we made all the conference sessions accessible online to participants around the world. Topics * 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
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