The 2nd Concept Discovery in Unstructured Data workshop (CDUD). Date: 06-10.05.2012. Site: Leuven, Belgium. Deadline: 01.02.2012
Concept discovery is a Knowledge Discovery in Databases (KDD) research field that uses human-centered techniques such as Formal Concept Analysis (FCA), Biclustering, Triclustering, Conceptual Graphs etc. for gaining insight into the underlying conceptual structure of the data. Traditional machine learning techniques are mainly focusing on structured data whereas most data available resides in unstructured, often textual, form. Compared to traditional data mining techniques, human-centered instruments actively engage the domain expert in the discovery process.
This workshop welcomes papers describing innovative research on data discovery techniques. Moreover, this workshop intends to provide a forum for researchers and developers of data mining instruments, working on issues associated with analyzing unstructured data.
- Conceptual Clustering, Biclustering, Triclustering etc.
- Data Mining (Text Mining, Graph Mining, Web Mining, Association Rules, Frequent
Closed Sets, etc.)
- Dealing with knowledge incompleteness and asymmetry
- Discovery techniques for conceptual models
- Efficient indexing and structuring algorithms.
- Formal Concept Analysis (FCA), Fuzzy FCA and Rough FCA
- Innovative applications of FCA for discovery purposes
- Knowledge discovery
- Ontology Learning from text
- Probabilistic concept discovery
- Sociological applications of FCA and related methods
- Structured methods for KDD
The maximum number of accepted papers by an individual author that can be covered by the workshop’s registration charge is 3. The papers over 12 pages are not allowed.