International Conference on Data Mining and Knowledge Discovery

• • Foundations of data mining
  • Data mining and machine learning algorithms and methods in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis), and in new areas
  • Mining text and semi-structured data, and mining temporal, spatial and multimedia data
  • Mining data streams
  • Mining spatio-temporal data
  • Mining with data clouds and Big Data
  • Link and graph mining
  • Pattern recognition and trend analysis
  • Collaborative filtering/personalization
  • Data and knowledge representation for data mining
  • Query languages and user interfaces for mining
  • Complexity, efficiency, and scalability issues in data mining
  • Data pre-processing, data reduction, feature selection and feature transformation
  • Post-processing of data mining results
  • Statistics and probability in large-scale data mining
  • Soft computing (including neural networks, fuzzy logic, evolutionary computation, and rough sets) and uncertainty management for data mining
  • Integration of data warehousing, OLAP and data mining
  • Human-machine interaction and visual data mining
  • High performance and parallel/distributed data mining
  • Quality assessment and interestingness metrics of data mining results
  • Visual Analytics
  • Security, privacy and social impact of data mining
  • Data mining applications in bioinformatics, electronic commerce, Web, intrusion detection, finance, marketing, healthcare, telecommunications and other fields