Suggestive 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