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. & Hilgenberg, K. (2013), SDCF - A Sensor Data Collection Framework for Social and Ubiquitous Environments: Challenges and First Experiences in Sensor-based Social Networks (Abstract), in 'Proc. Sunbelt XXXIII: Annual Meeting of the International Network for Social Network Analysis' , INSNA, Hamburg, Germany .
[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]

Atzmueller, M. & Hilgenberg, K. (2013), Towards Capturing Social Interactions with SDCF: An Extensible Framework for Mobile Sensing and Ubiquitous Data Collection, in 'Proc. 4th International Workshop on Modeling Social Media (MSM 2013), Hypertext 2013' , ACM Press, New York, NY, USA .
[BibTeX] [Endnote]

Jäschke, R. & Rudolph, S. (2013), Attribute Exploration on the Web, in Peggy Cellier; Felix Distel & Bernhard Ganter, ed., 'Contributions to the 11th International Conference on Formal Concept Analysis' , pp. 19--34 .
[BibTeX] [Endnote]

We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.

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]

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]

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', Informatik Spektrum 35 (2) , 132-135 .
[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. (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 .
[BibTeX] [Endnote]

Behrenbruch, K.; Atzmueller, M.; Evers, C.; Schmidt, L.; Stumme, G. & Geihs, K. (2012), A Personality Based Design Approach Using Subgroup Discovery 'Human-Centred Software Engineering' , Springer, Heidelberg, Germany , pp. 259--266 .
[BibTeX] [Endnote]

To facilitate user-centered software engineering, developers need an easy to grasp understanding of the user. The use of personas helps to keep specific user needs in mind during the design process. Technology acceptance is of particular interest for the design of innovative applications previously unknown to potential users. Therefore, our research focuses on defining a typology of relevant user characteristics with respect to technology acceptance and transferring those findings to the description of personas. The presented work focuses on the statistical relationship between technology acceptance and personality. We apply sub-group discovery as a statistical tool. Based on the statistically derived subgroups and patterns we define the mentioned personas to help developers to understand different forms of technology acceptance. By integrating the specifically defined personas into existing methods in the field of software engineering the feasibility of the presented approach is demonstrated.

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]

Scholz, C.; Atzmueller, M. & Stumme, G. (2012), On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties, in 'Proc. Fourth ASE/IEEE International Conference on Social Computing (SocialCom)' , IEEE Computer Society, Boston, MA, USA .
[BibTeX] [Endnote]

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.

2007

Clauset, A.; Shalizi, C. R. & Newman, M. E. J. (2007), 'Power-law distributions in empirical data' , cite arxiv:0706.1062Comment: 43 pages, 11 figures, 7 tables, 4 appendices; code available at http://www.santafe.edu/~aaronc/powerlaws/ .
[BibTeX] [Endnote]

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.