NFmcp @ ECML-PKDD 2016 

5th International Workshop on 

New Frontiers in Mining Complex Patterns 

Modern automatic systems are able to collect huge volumes of data, often with a complex structure (e.g. multi-table data, XML data, web data, time series and sequences, graphs and trees). This fact poses new challenges for current information systems with respect to storing, managing and mining these big sets of complex data.

The purpose of this workshop is to bring together researchers and practitioners of data mining who are interested in the advances and latest developments in the area of extracting patterns from big and complex data sources like blogs, event or log data, biological data, spatio-temporal data, social networks, mobility data, sensor data and streams, and so on. The workshop aims at integrating recent results from existing fields such as data mining, statistics, machine learning and relational databases to discuss and introduce new algorithmic foundations and representation formalisms in pattern discovery. We are interested in advanced techniques which preserve the informative richness of data and allow us to efficiently and efficaciously identify complex information units present in such data.

NFmcp 2016 calls for international contributions related to foundations, challenges and research opportunities raised by real-world learning and data mining problems in which the data as well as patterns are complex and heterogeneous. The goal of the workshop is to promote and publish research in the field of complex pattern mining. Suggested topics include (but not limited to) the following:
  • Foundations on pattern mining, pattern usage, and pattern understanding
  • Mining stream, time-series and sequence data
  • Mining networks and graphs
  • Mining biological data
  • Mining dynamic and evolving data
  • Mining environmental and scientific data
  • Mining heterogeneous and ubiquitous data
  • Mining multimedia data
  • Mining multi-relational data
  • Mining semi-structured and unstructured data
  • Mining spatio-temporal data
  • Mining Big Data
  • Social Media Analytics
  • Ontology and metadata
  • Privacy preserving mining
  • Semantic Web and Knowledge Databases