Call For Papers

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 2012 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 biological data
• Mining stream, time-series and sequence data
• Mining networks, graphs and trees
• Mining dynamic and evolving data
• Mining environmental and scientific data
• Mining heterogeneous data
• Mining multi-relational data
• Mining semi-structured and unstructured data
• Mining spatio-temporal data
• Mining linked, social and web data
• Ontology and metadata
• Privacy preserving mining
• Semantic Web and Knowledge Databases

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