NFmcp 2014 - 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 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 complex data sources like blogs, event or log data, medical 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.
Topic of Interest
NFMCP 2014 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
Social Media Analytics
Ontology and metadata
Privacy preserving mining
Semantic Web and Knowledge Databases
Structured Output Prediction