Atzmueller, M.: Analysis of Communities in Social Media. Proc. 20th Intl. Conference on Information and Knowledge Management (CIKM 2011). Glasgow, Scotland, UK: ACM Press, 2011

Atzmueller, M.: Data Mining. In: McCarthy, P. M. & Boonthum, C. (Hrsg.): Applied Natural Language Processing and Content Analysis: Advances in Identification, Investigation and Resolution.. IGI Global, 2011

Atzmueller, M.; Beer, S. & Puppe, F.: Data Mining, Validation and Collaborative Knowledge Capture. In: Brüggemann, S. & d’Amato, C. (Hrsg.): Collaboration and the Semantic Web: Social Networks, Knowledge Networks and Knowledge Resources.. IGI Global, 2011

Atzmueller, M.; Doerfel, S.; Hotho, A.; Mitzlaff, F. & Stumme, G.: Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players. Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011. to appear: 2011

Atzmueller, M. & Hotho, A. (Hrsg.): Proceedings of the 2011 International Workshop on Mining Ubiquitous and Social Environments (MUSE 2011). Athens, Greece: ECML/PKDD 2011, 2011

Atzmueller, M.; Benz, D.; Hotho, A. & Stumme, G.: Towards Mining Semantic Maturity in Social Bookmarking Systems. In: Passant, A.; Fernández, S.; Breslin, J. & Bojars, U. (Hrsg.): Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011). 2011
[Volltext]

Atzmueller, M. & Lemmerich, F.: VIKAMINE - A Rich-Client Environment for Intelligent Pattern Mining and Subgroup Discovery. Proceedings of the LWA 2011 - Learning, Knowledge, Adaptation. 2011

Björn-Elmar Macek, Martin Atzmueller, Gerd Stumme: Profile Mining in CVS-Logs and Face-to-Face Contacts for Recommending Software Developers. (2011),

Lemmerich, F. & Atzmueller, M.: Modeling Location-Based Profiles of Social Image Media using Explorative Pattern Mining. Proc. IEEE SocialCom 2011, Workshop on Modeling Social Media (MSM 2011). IEEE Computer Society, 2011

Mitzlaff, F.; Atzmueller, M.; Benz, D.; Hotho, A. & Stumme, G.: Community Assessment using Evidence Networks. Analysis of Social Media and Ubiquitous Data. 2011 (LNAI 6904)

Scholz, C.; Doerfel, S.; Atzmueller, M.; Hotho, A. & Stumme, G.: Resource-Aware On-Line RFID Localization Using Proximity Data. 2011

Atzmueller, M. & Hotho, A. (Hrsg.): Proceedings of the 2010 Workshop on Mining Ubiquitous and Social Environments (MUSE 2010). Barcelona, Spain: ECML/PKDD 2010, 2010

Atzmueller, M.; Benz, D.; Hotho, A. & Stumme, G. (Hrsg.): Proceedings of the LWA 2010 - Lernen, Wissen, Adaptivität. Department of Electrical Engineering/Computer Science, Kassel University, 2010Technical report (KIS), 2010-10

Atzmueller, M. & Roth-Berghofer, T.: Ready for the MACE? The Mining and Analysis Continuum of Explaining Uncovered. , 2010

Atzmueller, M.; Knauf, R.; Bode, S.; Farooq, Q.-U.-A. & Riebisch, M. (Hrsg.): The IWK2010 Workshops: DERIS2010 and EMDT2010. Proceedings of the IWK2010 Workshops: International Workshop on Design, Evaluation and Refinement of Intelligent Systems (DERIS2010) and the First International Workshop on Evolution Support for Model-Based Development and Testing (EMDT2010). RWTH Aachen University, 2010 (CEUR-WS 646)

Atzmueller, M. & Roth-Berghofer, T.: The Mining and Analysis Continuum of Explaining Uncovered. Proc. 30th SGAI International Conference on Artificial Intelligence (AI-2010). 2010

Atzmueller, M. & Roth-Berghofer, T.: Towards Explanation-Aware Social Software: Applying the Mining and Analysis Continuum of Explaining. Proc. Workshop on Explanation-aware Computing ExaCt 2010 @ ECAI 2010. 2010

Atzmueller, M. & Mitzlaff, F.: Towards Mining Descriptive Community Patterns. Workshop on Mining Patterns and Subgroups. Lorentz Center, Leiden, The Netherlands. Awarded with the Best Discovery Award, 2010

Atzmueller, M. & Beer, S.: Validation of Mixed-Structured Data Using Pattern Mining and Information Extraction. Proc. 55th IWK, International Workshop on Design, Evaluation and Refinement of Intelligent Systems (DERIS). University of Ilmenau, 2010

Berendt, B.; Hotho, A. & Stumme, G.: Bridging the Gap-Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0. In: Web Semantics: Science, Services and Agents on the World Wide Web 8 (2010), Nr. 2-3, S. 95 - 96
[Volltext]

Kaempgen, B.; Lemmerich, F. & Atzmueller, M.: Decision-Maker-Aware Design of Descriptive Data Mining. Proc. 55th IWK, International Workshop on Design, Evaluation and Refinement of Intelligent Systems (DERIS). 2010

Lemmerich, F. & Atzmueller, M.: Fast Discovery of Relevant Subgroup Patterns. Proc. 23rd FLAIRS Conference. 2010

Weiss, C. & Atzmueller, M.: EWMA Control Charts for Monitoring Binary Processes with Applications to Medical Diagnosis Data. In: Quality and Reliability Engineering (2010),

Hotho, A. & Stumme, G.: Mining the World Wide Web - Methods, Ap-
ications, and Perspectives. In: Künstliche Intelligenz (2007), Nr. 3, S. 5-8
[Volltext]

Themenheft Web Mining, Künstliche Intelligenz. , 2007
[Volltext]

Ackermann, M.; Berendt, B.; Grobelnik, M.; Hotho, A.; Mladenic, D.; Semeraro, G.; Spiliopoulou, M.; Stumme, G.; Svatek, V. & van Someren, M. (Hrsg.): Semantics, Web and Mining. Heidelberg: Springer, 2006
[Volltext]

Proc. of the European Web Mining Forum 2005. , 2005
[Volltext]

Berendt, B.; Hotho, A. & Stumme, G.: Semantic Web Mining and the Representation, Analysis, and Evolution of Web Space. In: Svatek, V. & Snasel, V. (Hrsg.): Proc. of the 1st Intl. Workshop on Representation and Analysis of Web Space. Technical University of Ostrava, 2005, S. 1-16
[Volltext]

Lakhal, L. & Stumme, G.: Efficient Mining of Association Rules Based on Formal Concept Analysis. LNAI Heidelberg, 2005
[Volltext]
Association rules are a popular knowledge discovery technique for
rehouse basket analysis. They indicate which items of the
rehouse are frequently bought together. The problem of association
le mining has first been stated in 1993. Five years later, several
search groups discovered that this problem has a strong connection
Formal Concept Analysis (FCA). In this survey, we will first
troduce some basic ideas of this connection along a specific
gorithm, and show how FCA helps in reducing the number
resulting rules without loss of information, before giving a
neral overview over the history and state of the art of applying
A for association rule mining.

Pasquier, N.; Taouil, R.; Bastide, Y.; Stumme, G. & Lakhal, L.: Generating a Condensed Representation for Association Rules. In: Journal Intelligent Information Systems (JIIS) 24 (2005), Nr. 1, S. 29-60
[Volltext]

Berendt, B.; Hotho, A.; Mladenic, D.; van Someren, M.; Spiliopoulou, M. & Stumme, G.: A Roadmap for Web Mining: From Web to Semantic Web.. In: Berendt, B.; Hotho, A.; Mladenic, D.; van Someren, M.; Spiliopoulou, M. & Stumme, G. (Hrsg.): Web Mining: From Web to Semantic Web. Heidelberg: Springer, 2004 (3209), S. 1-22
[Volltext]
The purpose of Web mining is to develop methods and systems for discovering models of objects and processes on the World Wide Web and for web-based systems that show adaptive performance. Web Mining integrates three parent areas: Data Mining (we use this term here also for the closely related areas of Machine Learning and Knowledge Discovery), Internet technology and World Wide Web, and for the more recent Semantic Web. The World Wide Web has made an enormous amount of information electronically accessible. The use of email, news and markup languages like HTML allow users to publish and read documents at a world-wide scale and to communicate via chat connections, including information in the form of images and voice records. The HTTP protocol that enables access to documents over the network via Web browsers created an immense improvement in communication and access to information. For some years these possibilities were used mostly in the scientific world but recent years have seen an immense growth in popularity, supported by the wide availability of computers and broadband communication. The use of the internet for other tasks than finding information and direct communication is increasing, as can be seen from the interest in ldquoe-activitiesrdquo such as e-commerce, e-learning, e-government, e-science.

Berendt, B.; Hotho, A. & Stumme, G.: Usage Mining for and on the Semantic Web. In: Kargupta, H.; Joshi, A.; Sivakumar, K. & Yesha, Y. (Hrsg.): Data Mining Next Generation Challenges and Future Directions. Boston: AAAI Press, 2004, S. 461-481
[Volltext]
Semantic Web Mining aims at combining the two fast-developing
search areas Semantic Web and Web Mining.
b Mining aims at discovering insights about the meaning of Web
sources and their usage. Given the primarily syntactical nature
data Web mining operates on, the discovery of meaning is
possible based on these data only. Therefore, formalizations of
e semantics of Web resources and navigation behavior are
creasingly being used. This fits exactly with the aims of the
mantic Web: the Semantic Web enriches the WWW by
chine-processable information which supports the user in his
sks. In this paper, we discuss the interplay of the Semantic Web
th Web Mining, with a specific focus on usage mining.

Web Mining: From Web to Semantic Web, First European Web
Mining Forum, EMWF 2003, Cavtat-Dubrovnik, Croatia, September
22, 2003, Revised Selected and Invited Papers. LNAI Heidelberg, 2004
[Volltext]