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LWDA/KDML 2016 : Workshop on Knowledge Discovery, Data Mining and Machine Learning

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Link: https://hpi.de//mueller/lwda-2016/call-for-papers/fg-kdml-2016.html
 
When Sep 12, 2016 - Sep 14, 2016
Where Potsdam, Germany
Submission Deadline Jun 15, 2016
Notification Due Jul 20, 2016
Final Version Due Aug 15, 2016
Categories    machine learning   data mining   knowledge discovery
 

Call For Papers

# Call for Papers: KDML 2016

## Workshop on Knowledge Discovery, Data Mining and Machine Learning

KDML is a series of workshops that aims to bring the German Machine Learning and Data Mining community together. The KDML Workshop is co-located with the annual LWDA 2016 – Learning, Knowledge, Data, Adaptation – conference (http://hpi.de/mueller/lwda-2016.html), which is going to take place in Potsdam, Germany from September 12th to 14th, 2016. We invite submissions on all aspects of data mining, knowledge discovery, and machine learning. Besides original research publications, also preliminary results and resubmissions of recently published articles are invited. KDML also explicitly invites student submission.

## Topics of interest include but are not limited to

- Foundations, algorithms, models, and theory of machine learning and data mining
- Methods of supervised, semi-supervised and unsupervised learning
- Multiobjective learning
- Rule-based learning and pattern mining
- Network and graph mining such as social networks and Linked Open Data
- Temporal, spatial & spatiotemporal data mining
- Text mining and mining, mining unstructured and semi-structured data
- Web mining
- Distributed data mining
- Data stream mining
- Visual analytics
- Big Data
- Deep learning
- Semantic in data mining and machine learning
- Applications of data mining in all domains including social, web, digital libraries, bioinformatics, and finance
- Tools for data mining and machine learning
- Open source frameworks and tools for data mining and machine learning

## Submissions

We solicit submissions under two different models:

1. full papers (peer-reviewed and to be published by LWDA, up to 12 pages)
2. presentations (peer-reviewed and to be published by LWDA in a 1-page abstract), e.g., recent publications at top international venues, visionary ideas, work in progress, demonstration systems, industrial challenges, ...

Please note, that w.r.t. both models authors will have the opportunity to give a presentation at KDML. For the first model, a full paper will be published in the LWDA proceedings. For the second model, an extended abstract (1 page) will be included in the LWDA proceedings. Submissions are welcome in English and German. However, submissions in English are preferred. All papers should be formatted according to the Springer LNCS guidelines and are to be submitted as PDF files to EasyChair. Please select the track *Knowledge Discovery, Data Mining and Machine Learning*.

All submissions (under both models) will be reviewed by at least two independent reviewers. The conference proceedings will be published as CEUR Workshop Proceedings and will be indexed by DBLP. All workshop participants have to register for the LWDA 2016 conference.

## Important Dates

- Submission deadline: June 15, 2016
- Acceptance notification: July 20, 2016
- Camera-ready copy: August 15, 2016
- LWDA 2016 Conference: September 12-14, 2016

For further information visit (http://hpi.de/mueller/lwda-2016.html), which is going to be permanently updated, or contact the program chairs via e-mail.

## Program Chairs

- Stephan Günnemann, Technical University of Munich (guennemann(at)in.tum.de)
- Ansgar Scherp, ZBW - Leibniz Information Centre for Economics and Kiel University (a.scherp(at)zbw.eu)

## Program Committee

- Martin Atzmueller
- Christian Bauckhage
- Martin Becker
- Alexander Dallmann
- Stephan Doerfel
- Marwan Hassani
- Alexander Hinneburg
- Andreas Hotho
- Robert Jäschke
- Kristian Kersting
- Ralf Krestel
- Florian Lemmerich
- Ulf Leser
- Hannes Mühleisen
- Thomas Niebler
- Nico Piatkowski
- Achim Rettinger
- Ute Schmid
- Robin Senge
- Gerd Stumme
- Arthur Zimek

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