During the last two decades, studies in Machine Learning have paved the way to the definition of efficient and stable data mining and knowledge discovery algorithms. Data mining and knowledge discovery can be considered today as stable fields with numerous efficient algorithms which have been proposed in order to extract knowledge in different forms from data.
Although, most existing data mining approaches look for patterns in tabular data (which
are typically obtained from relational databases), algorithmic extensions are recently investigated to new massive datasets representing complex interactions between several entities from heterogeneous and ubiquitous a variety of sources. These interactions may be spanned at multiple levels of granularity as well as at the spatial and/or temporal dimension.
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 area of extracting
complex patterns from text/hypertext data, networks and graphs, event or log data, biological data, spatio-temporal data, sensor data and streams, and so on. In particular, 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.


Topics of Interest

NFMCP 2013 calls for international contributions related to foundations, challenges and research opportunities raised by real-world learning and data mining problems in whichthe 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