Sonstiges
Conferator - a Social System for Conference and Contact Management.
Poster at INFORMATIK 2013. 2013.
Martin Atzmueller, Mark Kibanov, Christoph Scholz und Gerd Stumme.
[BibTeX]
Artikel in Zeitschriften
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), 2013.
Martin Atzmueller und Florian Lemmerich.
[BibTeX]
Artikel in Tagungsbänden
Subgroup Analytics and Interactive Assessment on Ubiquitous Data.
In:
Proceedings of the International Workshop on Mining Ubiquitous and Social Environments (MUSE2013).
Prague, Czech Republic, 2013.
Martin Atzmueller und Juergen Mueller.
[BibTeX]
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, 2013.
Mark Kibanov, Martin Atzmueller, Christoph Scholz und Gerd Stumme.
[BibTeX]
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, 2013.
Mark Kibanov, Martin Atzmueller, Christoph Scholz und Gerd Stumme.
[BibTeX]
Artikel in Zeitschriften
User-Relatedness and Community Structure in Social Interaction Networks.
CoRR/abs, 1309.3888, 2013.
Folke Mitzlaff, Martin Atzmueller, Dominik Benz, Andreas Hotho und Gerd Stumme.
[BibTeX]
Sonstiges
Proceedings of the 2013 International Smart University Workshop (SmartU 2013).
2013.
[BibTeX]
Artikel in Tagungsbänden
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, 2013.
Christoph Scholz, Martin Atzmueller, Mark Kibanov und Gerd Stumme.
[BibTeX]
Buchbeiträge
Mining Complex Event Patterns in Computer Networks.
In:
Postproceedings of the 1st Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2012.
Springer Verlag, Heidelberg, Germany, 2013.
Dietmar Seipel, Stefan Köhler, Philipp Neubeck und Martin Atzmueller.
[BibTeX]
Data Mining, Validation and Collaborative Knowledge Capture.
In:
S. Brüggemann und C. d’Amato (Herausgeber):
Collaboration and the Semantic Web: Social Networks, Knowledge Networks, and Knowledge Resources, Seiten 149-167.
IGI Global, 2012.
Martin Atzmueller, Stephanie Beer und Frank Puppe.
[BibTeX]
Artikel in Zeitschriften
Mining Social Media: Key Players, Sentiments, and Communities.
WIREs: Data Mining and Knowledge Discovery, In Press, 2012.
Martin Atzmueller.
[BibTeX]
Sonstiges
Modeling and Mining Ubiquitous Social Media.
2012.
[doi]
[BibTeX]
Artikel in Tagungsbänden
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, 2012.
Martin Atzmueller.
[BibTeX]
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, 2012.
Martin Atzmueller und Florian Lemmerich.
[doi]
[BibTeX]
Tagungsbände
Proceedings MSM 2012: Workshop on Modeling Social Media - Collective Intelligence in Social Media.
ACM Press, New York, NY, USA, 2012.
Alvin Chin, Martin Atzmueller und Denis Helic.
[BibTeX]
Buchbeiträge
Describing Locations using Tags and Images: Explorative Pattern Mining in Social Media.
In:
Modeling and Mining Ubiquitous Social Media.
Springer Verlag, Heidelberg, Germany, 2012.
Florian Lemmerich und Martin Atzmueller.
[doi]
[BibTeX]
Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research.
In:
P. Perner (Herausgeber):
Advances in Data Mining. Applications and Theoretical Aspects, Seiten 273-287.
Springer Berlin Heidelberg, 2012.
Jonas Poelmans, DmitryI. Ignatov, Stijn Viaene, Guido Dedene und SergeiO. Kuznetsov.
[doi]
[Kurzfassung]
[BibTeX]
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.
Artikel in Tagungsbänden
Mining Complex Event Patterns in Computer Networks.
In:
Proc. ECML/PKDD Workshop on New Frontiers in Mining Complex Patterns.
Bristol, UK, 2012.
Dietmar Seipel, Philipp Neubeck, Stefan Köhler und Martin Atzmueller.
[BibTeX]
On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission).
In:
Proc. LWA 2013 (KDML Special Track).
University of Bamberg, Bamberg, Germany, 2011.
Folke Mitzlaff, Martin Atzmueller, Gerd Stumme und Andreas Hotho.
[BibTeX]
Artikel in Zeitschriften
Temporal data mining using shape space representations of time series.
Neurocomputing, 74(1–3):379 - 393, 2010.
Artificial Brains
Erich Fuchs, Thiemo Gruber, Helmuth Pree und Bernhard Sick.
[doi]
[Kurzfassung]
[BibTeX]
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.