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 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