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CDUD 2016 : The 3d International Workshop on Concept Discovery in Unstructured Data

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Link: https://cla2016.hse.ru/cdud
 
When Jul 18, 2016 - Jul 22, 2016
Where Moscow, Russia
Submission Deadline May 23, 2016
Notification Due Jun 8, 2016
Final Version Due Jun 23, 2016
Categories    knowledge discovery   unstructured data   text mining   formal concept analysis
 

Call For Papers

In conjunction with CLA 2016: https://cla2016.hse.ru/

Concept discovery is a subdomain of Knowledge Discovery (KDD) that uses human-centered techniques such as Formal Concept Analysis (FCA), Topic Modeling, Visual Text Representations, 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. First, we are interested in methods for transforming unstructured into semi-structured information. Unstructured information such as texts or images can be tagged, keywords can be extracted from texts by means of Natural Language Processing methods, etc. For example, recently so-called Learning Representations such as Text Vectors or Visual Words have gained much attention in the domain of unstructured data. Second, in this workshop we also particularly welcome research on using human-centered instruments such as FCA to analyze unstructured and semi-structured data. Applications in which we are interested include but are not limited to Text Mining and Web Mining including forums, blogs, social sharing systems like Twitter and Facebook, mining sociological interviews, etc. We are also interested in innovative instruments for dealing with knowledge incompleteness and asymmetry.

Subject coverage:

- Applications of FCA for discovery purposes

- Association Rules and Frequent Closed Itemsets

- Biclustering and Multimodal clustering

- Conceptual Clustering

- Dealing with knowledge incompleteness and asymmetry

- Deep Learning for Text Representations

- Discovery techniques for conceptual models

- Efficient indexing and structuring algorithms

- Formal Concept Analysis

- Graph Mining

- Knowledge discovery and representation

- Natural Language Processing

- Ontology Learning from text

- Probabilistic concept discovery

- Text Kernels

- Text Mining

- Topic Modeling

- Visual Analytics

Workshop chairs

Dmitry Ignatov, National Research University Higher School of Economics, Russia

Dmitry Ilvovsky, National Research University Higher School of Economics, Russia

Alexander Panchenko, Tehcnische Universitat Darmstadt, Germany

Important dates

Submission deadline: May 23, 2016

Notification of acceptance: June 08, 2016

Camera-ready copy: June 23, 2016

Proceedings

All accepted papers will be included in the workshop’s proceedings to be published online on the CEUR-Workshop web site in a volume with ISSN and indexed by Scopus. Two previous editions are available at the CEUR-Workshop web site: http://ceur-ws.org/Vol-757/ and http://ceur-ws.org/Vol-871/.

Submission Procedure

Electronic version of full paper complete with authors’ affiliations should be submitted through the conference electronic submission system.
Use the submission link http://www.easychair.org/conferences/?conf=cdud2016.
Manuscripts must be prepared with LaTeX or Microsoft Office and should follow the Springer format available at http://www.springer.de/comp/lncs/authors.html.
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.

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SIMPDA 2016   6th Int. Symposium on Data-driven Process Discovery and Analysis
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