Atzmueller, M.; Becker, M.; Kibanov, M.; Scholz, C.; Doerfel, S.; Hotho, A.; Macek, B.-E.; Mitzlaff, F.; Mueller, J. & Stumme, G. (2014), 'Ubicon and its Applications for Ubiquitous Social Computing', New Review of Hypermedia and Multimedia 20 (1) , 53--77 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.

Cellier, P.; Charnois, T.; Hotho, A.; Matwin, S.; Moens, M. & Toussaint, Y., ed. (2014), Proceedings of the 1st International Workshop on Interactions between
Data Mining and Natural Language Processing co-located with The European
Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases, DMNLP@PKDD/ECML 2014, Nancy, France, September
15, 2014 , Vol. 1202 , CEUR-WS.org .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

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

Atzmueller, M. & Hotho, A., ed. (2012), Proceedings of the Third International Workshop on Mining Ubiquitous and Social Environments (MUSE 2012) , Workshop Notes , Bristol, UK .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Han, J.; Kamber, M. & Pei, J. (2012), 'Data mining concepts and techniques, third edition' , Morgan Kaufmann Publishers , Waltham, Mass. .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

2011

Atzmueller, M. & Hotho, A., ed. (2011), Proceedings of the 2011 International Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) , ECML/PKDD 2011 , Athens, Greece .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Atzmueller, M.; Benz, D.; Hotho, A. & Stumme, G. (2011), Towards Mining Semantic Maturity in Social Bookmarking Systems, in Alexandre Passant; Sergio Fernández; John Breslin & Uldis Bojārs, ed., 'Proceedings of the 4th international workshop on Social Data on the Web (SDoW2011)' .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Bloehdorn, S.; Blohm, S.; Cimiano, P.; Giesbrecht, E.; Hotho, A.; Lösch, U.; Mädche, A.; Mönch, E.; Sorg, P.; Staab, S. & Völker, J. (2011), Combining Data-Driven and Semantic Approaches for Text Mining., in Dieter Fensel, ed., 'Foundations for the Web of Information and Services' , Springer, , pp. 115-142 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Hotho, A. & Stumme, G. (2011), From Semantic Web Mining to Social and Ubiquitous Mining - A Subjective View on Past, Current, and Future Research., in Dieter Fensel, ed., 'Foundations for the Web of Information and Services' , Springer, , pp. 143-153 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Mitzlaff, F.; Atzmueller, M.; Benz, D.; Hotho, A. & Stumme, G. (2011), Community Assessment using Evidence Networks, in 'Analysis of Social Media and Ubiquitous Data' .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

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 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Rubin, T. N.; Chambers, A.; Smyth, P. & Steyvers, M. (2011), 'Statistical Topic Models for Multi-Label Document Classification' , cite arxiv:1107.2462 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Machine learning approaches to multi-label document classification have (to date) largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This problem is amplified when the label frequencies exhibit the type of highly skewed distributions that are often observed in real-world datasets. In this paper we investigate a class of generative statistical topic models for multi-label documents that associate individual word tokens with different labels. We investigate the advantages of this approach relative to discriminative models, particularly with respect to classification problems involving large numbers of relatively rare labels. We compare the performance of generative and discriminative approaches on document labeling tasks ranging from datasets with several thousand labels to datasets with tens of labels. The experimental results indicate that generative models can achieve competitive multi-label classification performance compared to discriminative methods, and have advantages for datasets with many labels and skewed label frequencies.

2010

Hotho, A.; Ulslev Pedersen, R. & Wurst, M. (2010), 'Ubiquitous Data', Lecture Notes in Computer Science (6202) , 61--74 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Yassine, M. & Hajj, H. (2010), A Framework for Emotion Mining from Text in Online Social Networks., in Wei Fan; Wynne Hsu; Geoffrey I. Webb; Bing Liu; Chengqi Zhang; Dimitrios Gunopulos & Xindong Wu, ed., 'ICDM Workshops' , IEEE Computer Society, , pp. 1136-1142 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

2009

Srivastava, A. & Sahami, M. (2009), Text mining : classification, clustering, and applications , CRC Press , Boca Raton, FL .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]

Giving a broad perspective of the field from numerous vantage points, 'Text Mining' focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas.

2006

2005

Abstract. This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much more complex challenge. Recently, significant effort has been invested in building sets of personalized PageRank vectors. PageRank is also used in many diverse applications other than ranking. We are interested in the theoretical foundations of the PageRank formulation, in the acceleration of PageRank computing, in the effects of particular aspects of web graph structure on the optimal organization of computations, and in PageRank stability. We also review alternative models that lead to authority indices similar to PageRank and the role of such indices in applications other than web search. We also discuss linkbased search personalization and outline some aspects of PageRank infrastructure from associated measures of convergence to link preprocessing. 1.

1996

Fayyad, U. M.; Piatetsky-Shapiro, G. & Smyth, P. (1996), Advances in knowledge discovery and data mining, in Usama M. Fayyad; Gregory Piatetsky-Shapiro; Padhraic Smyth & Ramasamy Uthurusamy, ed., , American Association for Artificial Intelligence, Menlo Park, CA, USA , pp. 1--34 .
[Volltext] [Kurzfassung] [BibTeX] [Endnote]