Atzmueller, M.; Kibanov, M.; Scholz, C. & Stumme, G. (2013), 'Conferator - a Social System for Conference and Contact Management' , Technical report, University of Koblenz-Landau , INFORMATIK 2013 , Poster at INFORMATIK 2013 .
[BibTeX] [Endnote]

Atzmueller, M. & Lemmerich, F. (2013), 'Exploratory Pattern Mining on Social Media using Geo-References and Social Tagging Information', International Journal of Web Science (Special Issue on Social Web Search and Mining) 2 (1/2) .
[BibTeX] [Endnote]

Atzmueller, M. & Mueller, J. (2013), Subgroup Analytics and Interactive Assessment on Ubiquitous Data, in 'Proceedings of the International Workshop on Mining Ubiquitous and Social Environments (MUSE2013)' .
[BibTeX] [Endnote]

Kibanov, M.; Atzmueller, M.; Scholz, C. & Stumme, G. (2013), Evolution of Contacts and Communities in Networks of Face-to-Face Proximity (Extended Abstract, Resubmission), in 'Proc. LWA 2013 (KDML Special Track)' , University of Bamberg, Bamberg, Germany .
[BibTeX] [Endnote]

Kibanov, M.; Atzmueller, M.; Scholz, C. & Stumme, G. (2013), On the Evolution of Contacts and Communities in Networks of Face-to-Face Proximity, in 'Proc. IEEE CPSCom 2013' , IEEE Computer Society, Boston, MA, USA .
[BibTeX] [Endnote]

Mitzlaff, F.; Atzmueller, M.; Benz, D.; Hotho, A. & Stumme, G. (2013), 'User-Relatedness and Community Structure in Social Interaction Networks', CoRR/abs 1309.3888 .
[BibTeX] [Endnote]

Roth-Berghofer, T.; Oussena, S. & Atzmueller, M., ed. (2013), Proceedings of the 2013 International Smart University Workshop (SmartU 2013) , CONTEXT 2013 , Annecy, France .
[BibTeX] [Endnote]

Scholz, C.; Atzmueller, M.; Kibanov, M. & Stumme, G. (2013), How Do People Link? Analysis of Contact Structures in Human Face-to-Face Proximity Networks, in 'Proc. ASONAM 2013' , ACM Press, New York, NY, USA .
[BibTeX] [Endnote]

Seipel, D.; Köhler, S.; Neubeck, P. & Atzmueller, M. (2013), Mining Complex Event Patterns in Computer Networks 'Postproceedings of the 1st Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2012' , Springer Verlag, Heidelberg, Germany .
[BibTeX] [Endnote]

2012

Atzmueller, M.; Beer, S. & Puppe, F. (2012), Data Mining, Validation and Collaborative Knowledge Capture, in Stefan Brüggemann & Claudia d’Amato, ed., 'Collaboration and the Semantic Web: Social Networks, Knowledge Networks, and Knowledge Resources' , IGI Global, , pp. 149-167 .
[BibTeX] [Endnote]

Atzmueller, M. (2012), 'Mining Social Media: Key Players, Sentiments, and Communities', WIREs: Data Mining and Knowledge Discovery In Press .
[BibTeX] [Endnote]

Atzmueller, M.; Chin, A.; Helic, D. & Hotho, A., ed. (2012), Modeling and Mining Ubiquitous Social Media , Vol. 7472 , Springer Verlag , Heidelberg, Germany .
[BibTeX] [Endnote]

Atzmueller, M. (2012), Onto Collective Intelligence in Social Media: Exemplary Applications and Perspectives, in 'Proc. 3rd International Workshop on Modeling Social Media (MSM 2012), Hypertext 2012' , ACM Press, New York, NY, USA .
[BibTeX] [Endnote]

Atzmueller, M. & Lemmerich, F. (2012), VIKAMINE - Open-Source Subgroup Discovery, Pattern Mining, and Analytics, in 'Proc. ECML/PKDD 2012: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. accepted' , Springer Verlag, Heidelberg, Germany .
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Chin, A.; Atzmueller, M. & Helic, D., ed. (2012), Proceedings MSM 2012: Workshop on Modeling Social Media -- Collective Intelligence in Social Media , ACM Press , New York, NY, USA .
[BibTeX] [Endnote]

Lemmerich, F. & Atzmueller, M. (2012), Describing Locations using Tags and Images: Explorative Pattern Mining in Social Media 'Modeling and Mining Ubiquitous Social Media' , Springer Verlag, Heidelberg, Germany .
[BibTeX] [Endnote]

Poelmans, J.; Ignatov, D.; Viaene, S.; Dedene, G. & Kuznetsov, S. (2012), Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research, in Petra Perner, ed., 'Advances in Data Mining. Applications and Theoretical Aspects' , Springer Berlin Heidelberg, , pp. 273-287 .
[BibTeX] [Endnote]

Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.

Seipel, D.; Neubeck, P.; Köhler, S. & Atzmueller, M. (2012), Mining Complex Event Patterns in Computer Networks, in 'Proc. ECML/PKDD Workshop on New Frontiers in Mining Complex Patterns' .
[BibTeX] [Endnote]

2011

Mitzlaff, F.; Atzmueller, M.; Stumme, G. & Hotho, A. (2011), On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission), in 'Proc. LWA 2013 (KDML Special Track)' , University of Bamberg, Bamberg, Germany .
[BibTeX] [Endnote]

2010

Fuchs, E.; Gruber, T.; Pree, H. & Sick, B. (2010), 'Temporal data mining using shape space representations of time series', Neurocomputing 74 (1–3) , 379 - 393 .
[BibTeX] [Endnote]

Subspace representations that preserve essential information of high-dimensional data may be advantageous for many reasons such as improved interpretability, overfitting avoidance, acceleration of machine learning techniques. In this article, we describe a new subspace representation of time series which we call polynomial shape space representation. This representation consists of optimal (in a least-squares sense) estimators of trend aspects of a time series such as average, slope, curve, change of curve, etc. The shape space representation of time series allows for a definition of a novel similarity measure for time series which we call shape space distance measure. Depending on the application, time series segmentation techniques can be applied to obtain a piecewise shape space representation of the time series in subsequent segments. In this article, we investigate the properties of the polynomial shape space representation and the shape space distance measure by means of some benchmark time series and discuss possible application scenarios in the field of temporal data mining.