NFmcp @ ECML-PKDD 2018 

7th 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. The massive and complex data pose new challenges for current research in Knowledge Discovery and Data Mining. They require new methods for storing, managing and analyzing them by taking into account various complexity aspects: Complex structures (e.g. multi-relational, time series and sequences, networks, and trees) as input/output of the data mining process; Massive amounts of high dimensional data collections flooding as high-speed streams and requiring (near) real time processing and model adaptation to concept drifts; New application scenarios involving security issues, interaction with other entities and real-time response toevents triggered by sensors.

The purpose of the workshop is to bring together researchers and practitioners of data mining and machine learning interested in analysis of complex and massive data sources such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams.

Submissions discussing and introducing new algorithmic foundations and representation formalisms in complex pattern discovery are also welcome. We encourage submissions from the areas of statistics, machine learning and big data analytics, which present techniques that take advantage of the informative richness of complex massive data for efficiently and effectively identifying new patterns. Finally, submissions describing preliminary and promising studies are also welcome.

A non-exclusive list of topics for the complex pattern mining research is reported in 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
  • Data Mining for Cybersecurity

Methodologically, the solutions can be rooted in different research fields, such as, pattern mining, neural networks and deep learning, probabilistic inference, decision trees, rule learning, reinforcement learning etc.