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    AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
    Atzmueller, M., Kibanov, M., Scholz, C. & Stumme, G. Conferator - a Social System for Conference and Contact Management

    [BibTeX]

    		 2013 Poster at INFORMATIK 2013  misc  
    BibTeX: 
    @misc{atzmueller2013conferator,
  author = {Atzmueller, Martin and Kibanov, Mark and Scholz, Christoph and Stumme, Gerd},
  title = {Conferator - a Social System for Conference and Contact Management},
  publisher = {INFORMATIK 2013},
  year = {2013}
}
    Atzmueller, M. & Hilgenberg, K. SDCF - A Sensor Data Collection Framework for Social and Ubiquitous Environments: Challenges and First Experiences in Sensor-based Social Networks (Abstract)

    [BibTeX]

    		 2013 Proc. Sunbelt XXXIII: Annual Meeting of the International Network for Social Network Analysis  inproceedings  
    BibTeX: 
    @inproceedings{atzmueller2013sensor,
  author = {Atzmueller, Martin and Hilgenberg, Katy},
  title = {SDCF - A Sensor Data Collection Framework for Social and Ubiquitous Environments: Challenges and First Experiences in Sensor-based Social Networks (Abstract)},
  booktitle = {Proc. Sunbelt XXXIII: Annual Meeting of the International Network for Social Network Analysis},
  publisher = {INSNA},
  year = {2013}
}
    Atzmueller, M. & Mueller, J. Subgroup Analytics and Interactive Assessment on Ubiquitous Data

    [BibTeX]

    		 2013 Proceedings of the International Workshop on Mining Ubiquitous and Social Environments (MUSE2013)  inproceedings  
    BibTeX: 
    @inproceedings{atzmueller2013subgroup,
  author = {Atzmueller, Martin and Mueller, Juergen},
  title = {Subgroup Analytics and Interactive Assessment on Ubiquitous Data},
  booktitle = {Proceedings of the International Workshop on Mining Ubiquitous and Social Environments (MUSE2013)},
  year = {2013}
}
    Atzmueller, M. & Hilgenberg, K. Towards Capturing Social Interactions with SDCF: An Extensible Framework for Mobile Sensing and Ubiquitous Data Collection

    [BibTeX]

    		 2013 Proc. 4th International Workshop on Modeling Social Media (MSM 2013), Hypertext 2013  inproceedings  
    BibTeX: 
    @inproceedings{atzmueller2013towards,
  author = {Atzmueller, Martin and Hilgenberg, Katy},
  title = {Towards Capturing Social Interactions with SDCF: An Extensible Framework for Mobile Sensing and Ubiquitous Data Collection},
  booktitle = {Proc. 4th International Workshop on Modeling Social Media (MSM 2013), Hypertext 2013},
  publisher = {ACM Press},
  year = {2013}
}
    Jäschke, R. & Rudolph, S. Attribute Exploration on the Web

    [Abstract] [BibTeX]

    		 2013 Contributions to the 11th International Conference on Formal Concept Analysis, pp. 19-34  inproceedings URL 
    Abstract: 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.
    BibTeX: 
    @inproceedings{jaschke2013attribute,
  author = {Jäschke, Robert and Rudolph, Sebastian},
  title = {Attribute Exploration on the Web},
  booktitle = {Contributions to the 11th International Conference on Formal Concept Analysis},
  year = {2013},
  pages = {19--34},
  url = {http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113133}
}
    Kibanov, M., Atzmueller, M., Scholz, C. & Stumme, G. Evolution of Contacts and Communities in Networks of Face-to-Face Proximity (Extended Abstract, Resubmission)

    [BibTeX]

    		 2013 Proc. LWA 2013 (KDML Special Track)  inproceedings  
    BibTeX: 
    @inproceedings{kibanov2013evolution,
  author = {Kibanov, Mark and Atzmueller, Martin and Scholz, Christoph and Stumme, Gerd},
  title = {Evolution of Contacts and Communities in Networks of Face-to-Face Proximity (Extended Abstract, Resubmission)},
  booktitle = {Proc. LWA 2013 (KDML Special Track)},
  publisher = {University of Bamberg},
  year = {2013}
}
    Mitzlaff, F., Atzmueller, M., Benz, D., Hotho, A. & Stumme, G. User-Relatedness and Community Structure in Social Interaction Networks

    [BibTeX]

    		 2013 CoRR/abs
    Vol. 1309.3888     		 article  
    BibTeX: 
    @article{mitzlaff2013userrelatedness,
  author = {Mitzlaff, Folke and Atzmueller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
  title = {User-Relatedness and Community Structure in Social Interaction Networks},
  journal = {CoRR/abs},
  year = {2013},
  volume = {1309.3888}
}
    Atzmueller, M., Beer, S. & Puppe, F. Data Mining, Validation and Collaborative Knowledge Capture

    [BibTeX]

    		 2012 Collaboration and the Semantic Web: Social Networks, Knowledge Networks, and Knowledge Resources, pp. 149-167  incollection  
    BibTeX: 
    @incollection{ABP:11,
  author = {Atzmueller, Martin and Beer, Stephanie and Puppe, Frank},
  title = {Data Mining, Validation and Collaborative Knowledge Capture},
  booktitle = {Collaboration and the Semantic Web: Social Networks, Knowledge Networks, and Knowledge Resources},
  publisher = {IGI Global},
  year = {2012},
  pages = {149-167}
}
    Atzmueller, M. Mining Social Media

    [BibTeX]

    		 2012 Informatik Spektrum
    Vol. 35(2), pp. 132-135     		 article  
    BibTeX: 
    @article{Atzmueller:12,
  author = {Atzmueller, Martin},
  title = {Mining Social Media},
  journal = {Informatik Spektrum},
  year = {2012},
  volume = {35},
  number = {2},
  pages = {132-135}
}
    Atzmueller, M. Mining Social Media: Key Players, Sentiments, and Communities

    [BibTeX]

    		 2012 WIREs: Data Mining and Knowledge Discovery
    Vol. In Press     		 article  
    BibTeX: 
    @article{Atzmueller:12c,
  author = {Atzmueller, Martin},
  title = {Mining Social Media: Key Players, Sentiments, and Communities},
  journal = {WIREs: Data Mining and Knowledge Discovery},
  year = {2012},
  volume = {In Press}
}
    Atzmueller, M. Onto Collective Intelligence in Social Media: Exemplary Applications and Perspectives

    [BibTeX]

    		 2012 Proc. 3rd International Workshop on Modeling Social Media (MSM 2012), Hypertext 2012  inproceedings  
    BibTeX: 
    @inproceedings{Atzmueller:12b,
  author = {Atzmueller, Martin},
  title = {Onto Collective Intelligence in Social Media: Exemplary Applications and Perspectives},
  booktitle = {Proc. 3rd International Workshop on Modeling Social Media (MSM 2012), Hypertext 2012},
  publisher = {ACM Press},
  year = {2012}
}
    Atzmueller, M. & Lemmerich, F. VIKAMINE - Open-Source Subgroup Discovery, Pattern Mining, and Analytics

    [BibTeX]

    		 2012 Proc. ECML/PKDD 2012: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. accepted  inproceedings URL 
    BibTeX: 
    @inproceedings{AL:12a,
  author = {Atzmueller, Martin and Lemmerich, Florian},
  title = {VIKAMINE - Open-Source Subgroup Discovery, Pattern Mining, and Analytics},
  booktitle = {Proc. ECML/PKDD 2012: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. accepted},
  publisher = {Springer Verlag},
  year = {2012},
  url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-vikamine2-ecml-pkdd-2012.pdf}
}
    Behrenbruch, K., Atzmueller, M., Evers, C., Schmidt, L., Stumme, G. & Geihs, K. A Personality Based Design Approach Using Subgroup Discovery

    [Abstract] [BibTeX]

    		 2012
    Vol. 7623Human-Centred Software Engineering, pp. 259-266      		 incollection  
    Abstract: 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.
    BibTeX: 
    @incollection{BAESSG:12,
  author = {Behrenbruch, Kay and Atzmueller, Martin and Evers, Christoph and Schmidt, Ludger and Stumme, Gerd and Geihs, Kurt},
  title = {A Personality Based Design Approach Using Subgroup Discovery},
  booktitle = {Human-Centred Software Engineering},
  publisher = {Springer},
  year = {2012},
  volume = {7623},
  pages = {259--266 }
}
    Proceedings MSM 2012: Workshop on Modeling Social Media -- Collective Intelligence in Social Media

    [BibTeX]

    		 2012   proceedings  
    BibTeX: 
    @proceedings{CAH:12,,
  title = {Proceedings MSM 2012: Workshop on Modeling Social Media -- Collective Intelligence in Social Media},
  publisher = {ACM Press},
  year = {2012}
}
    Lemmerich, F. & Atzmueller, M. Describing Locations using Tags and Images: Explorative Pattern Mining in Social Media

    [BibTeX]

    		 2012
    Vol. 7472Modeling and Mining Ubiquitous Social Media     		 incollection URL 
    BibTeX: 
    @incollection{LA:12,
  author = {Lemmerich, Florian and Atzmueller, Martin},
  title = {Describing Locations using Tags and Images: Explorative Pattern Mining in Social Media},
  booktitle = {Modeling and Mining Ubiquitous Social Media},
  publisher = {Springer Verlag},
  year = {2012},
  volume = {7472},
  url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/lemmerich-explorative-pattern-mining-socia-media-lnai-2012.pdf}
}
    Scholz, C., Atzmueller, M. & Stumme, G. On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties

    [BibTeX]

    		 2012 Proc. Fourth ASE/IEEE International Conference on Social Computing (SocialCom)  inproceedings URL 
    BibTeX: 
    @inproceedings{SAS:12,
  author = {Scholz, Christoph and Atzmueller, Martin and Stumme, Gerd},
  title = {On the Predictability of Human Contacts: Influence Factors and the Strength of Stronger Ties},
  booktitle = {Proc. Fourth ASE/IEEE International Conference on Social Computing (SocialCom)},
  publisher = {IEEE Computer Society},
  year = {2012},
  url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/scholz-on-f2f-predictability-socialcom-2012.pdf}
}
    Seipel, D., Neubeck, P., Köhler, S. & Atzmueller, M. Mining Complex Event Patterns in Computer Networks

    [BibTeX]

    		 2012 Proc. ECML/PKDD Workshop on New Frontiers in Mining Complex Patterns  inproceedings  
    BibTeX: 
    @inproceedings{SNKA:12,
  author = {Seipel, Dietmar and Neubeck, Philipp and Köhler, Stefan and Atzmueller, Martin},
  title = {Mining Complex Event Patterns in Computer Networks},
  booktitle = {Proc. ECML/PKDD Workshop on New Frontiers in Mining Complex Patterns},
  year = {2012}
}
    Mitzlaff, F., Atzmueller, M., Stumme, G. & Hotho, A. On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)

    [BibTeX]

    		 2011 Proc. LWA 2013 (KDML Special Track)  inproceedings  
    BibTeX: 
    @inproceedings{mitzlaff2011semantics,
  author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas},
  title = {On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission)},
  booktitle = {Proc. LWA 2013 (KDML Special Track)},
  publisher = {University of Bamberg},
  year = {2011}
}
    Fuchs, E., Gruber, T., Pree, H. & Sick, B. Temporal data mining using shape space representations of time series

    [Abstract] [BibTeX]

    		 2010 Neurocomputing
    Vol. 74(1–3), pp. 379 - 393     		 article DOI URL 
    Abstract: 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.
    BibTeX: 
    @article{Fuchs2010379,
  author = {Fuchs, Erich and Gruber, Thiemo and Pree, Helmuth and Sick, Bernhard},
  title = {Temporal data mining using shape space representations of time series},
  journal = {Neurocomputing},
  year = {2010},
  volume = {74},
  number = {1–3},
  pages = {379 - 393},
  note = {Artificial Brains},
  url = {http://www.sciencedirect.com/science/article/pii/S0925231210002237},
  doi = {http://dx.doi.org/10.1016/j.neucom.2010.03.022}
}
    Clauset, A., Shalizi, C.R. & Newman, M.E.J. Power-law distributions in empirical data

    [Abstract] [BibTeX]

    		 2007   misc DOI URL 
    Abstract: Power-law distributions occur in many situations of scientific interest and
    ve significant consequences for our understanding of natural and man-made
    enomena. Unfortunately, the detection and characterization of power laws is
    mplicated by the large fluctuations that occur in the tail of the
    stribution -- the part of the distribution representing large but rare events
    and by the difficulty of identifying the range over which power-law behavior
    lds. Commonly used methods for analyzing power-law data, such as
    ast-squares fitting, can produce substantially inaccurate estimates of
    rameters for power-law distributions, and even in cases where such methods
    turn accurate answers they are still unsatisfactory because they give no
    dication of whether the data obey a power law at all. Here we present a
    incipled statistical framework for discerning and quantifying power-law
    havior in empirical data. Our approach combines maximum-likelihood fitting
    thods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic
    d likelihood ratios. We evaluate the effectiveness of the approach with tests
    synthetic data and give critical comparisons to previous approaches. We also
    ply the proposed methods to twenty-four real-world data sets from a range of
    fferent disciplines, each of which has been conjectured to follow a power-law
    stribution. In some cases we find these conjectures to be consistent with the
    ta while in others the power law is ruled out.
    BibTeX: 
    @misc{clauset2007powerlaw,
  author = {Clauset, Aaron and Shalizi, Cosma Rohilla and Newman, M. E. J.},
  title = {Power-law distributions in empirical data},
  year = {2007},
  note = {cite arxiv:0706.1062Comment: 43 pages, 11 figures, 7 tables, 4 appendices; code available at  http://www.santafe.edu/~aaronc/powerlaws/},
  url = {http://arxiv.org/abs/0706.1062},
  doi = {http://dx.doi.org/10.1137/070710111}
}

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