NFmcp @ ECML-PKDD 2020
9th 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 (e.g. multi-table data, semi-structured data, web
data, time series and sequences, graphs and trees). 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
to events 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 data. We welcome submissions focusing on recent
advances and latest developments in the 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 data mining
- Semantic web and Knowledge databases
- Data Mining for Cybersecurity
