@article{boley98principal,
        title = {Principal Direction Divisive Partitioning},
        author = {Daniel Boley},
        journal = {Data Mining and Knowledge Discovery},
        number = 4,
        pages = {325-344},
        volume = 2,
        year = 1998,
        url = {citeseer.ist.psu.edu/boley97principal.html},
        description = {Principal Direction Divisive Partitioning - Boley (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/2b3c83d7f8c26c7ea645092eac767abca/grahl},
	keywords = {clustering}
}

@inproceedings{860549,
        title = {Generating hierarchical summaries for web searches},
        address = {New York, NY, USA},
        author = {Dawn J. Lawrie and W. Bruce Croft},
        booktitle = {SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval},
        pages = {457--458},
        publisher = {ACM Press},
        year = 2003,
        url = {http://portal.acm.org/citation.cfm?id=860435.860549&coll=&dl=&type=series&idx=860435&part=Proceedings&WantType=Proceedings&title=Annual%20ACM%20Conference%20on%20Research%20and%20Development%20in%20Information%20Retrieval&CFID=15151515&CFTOKEN=6184618},
        location = {Toronto, Canada},
	isbn = {1-58113-646-3},
	doi = {http://doi.acm.org/10.1145/860435.860549},
	description = {: SIGIR '03, Generating hierarchical summaries for ...},
	abstract = {Hierarchies provide a means of organizing, summarizing and accessing information. We describe a method for automatically generating hierarchies from small collections of text, and then apply this technique to summarizing the documents retrieved by a search engine.},
	biburl = {http://www.bibsonomy.org/bibtex/2519d345504436bab425c0c8ad5d89a91/grahl},
	keywords = {croft clustering text}
}

@inproceedings{sanderson99deriving,
        title = {Deriving Concept Hierarchies from Text},
        author = {Mark Sanderson and W. Bruce Croft},
        booktitle = {Research and Development in Information Retrieval},
        pages = {206-213},
        year = 1999,
        url = {citeseer.ist.psu.edu/sanderson99deriving.html},
        description = {Deriving Concept Hierarchies From Text - Sanderson, Croft (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/2c562ccc8d54fb6c3f32dcbe722cef386/grahl},
	keywords = {conceptual clustering text}
}

@inproceedings{347123,
        title = {Efficient clustering of high-dimensional data sets with application to reference matching},
        address = {New York, NY, USA},
        author = {Andrew McCallum and Kamal Nigam and Lyle H. Ungar},
        booktitle = {KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining},
        pages = {169--178},
        publisher = {ACM Press},
        year = 2000,
        location = {Boston, Massachusetts, United States},
	isbn = {1-58113-233-6},
	doi = {http://doi.acm.org/10.1145/347090.347123},
	biburl = {http://www.bibsonomy.org/bibtex/2346d1db87c3bda5fcf4ec5f92a75e16a/grahl},
	keywords = {clustering}
}

@inproceedings{schmitz2006content,
        title = {Content Aggregation on Knowledge Bases using Graph Clustering},
        address = {Budva, Montenegro},
        author = {Christoph Schmitz and Andreas Hotho and Robert Jäschke and Gerd Stumme},
        booktitle = {Proceedings of the 3rd European Semantic Web Conference},
        month = {June},
        year = 2006,
        biburl = {http://www.bibsonomy.org/bibtex/27d738e62dffd04f709e66de94c6dee89/grahl},
	keywords = {ontology clustering aggregation graphtheory content}
}

@inproceedings{schmitz2005towards,
        title = {Towards Content Aggregation on Knowledge Bases through Graph Clustering},
        address = {Wörlitz},
        author = {Christoph Schmitz},
        booktitle = {Proc. 17. GI-Workshop ``Grundlagen von Datenbanken''},
        editor = {Stefan Braß and Christian Goldberg},
        month = {May},
        year = 2005,
        url = {http://www.kde.cs.uni-kassel.de/schmitz/publ/gvd2005_schmitz.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/2d4de2a97be96bef44e907de9ddf7719e/grahl},
	keywords = {graph clustering knowledge base}
}

@article{nejdl2004superpeer2,
        title = {Super-Peer-Based Routing Strategies for {RDF}-Based
                  Peer-to-Peer Networks},
        author = {Wolfgang Nejdl and Martin Wolpers and Wolf Siberski and Christoph Schmitz and Mario Schlosser and Ingo Brunkhorst and Alexander Löser},
        journal = {Journal of Web Semantics},
        month = {February},
        volume = {Special issue WWW 2003},
        year = 2004,
        url = {http://www.kde.cs.uni-kassel.de/schmitz/publ/2003-11-18.semweb.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/25a5f7698713bb9af488805e9b88c4922/grahl},
	keywords = {routing clustering p2p}
}

@inproceedings{nejdl2003superpeer,
        title = {Super-Peer-Based Routing and Clustering Strategies
                  for {RDF}-Based Peer-To-Peer Networks},
        address = {Budapest},
        author = {Wolfgang Nejdl and Martin Wolpers and Wolf Siberski and Christoph Schmitz and Mario Schlosser and Ingo Brunkhorst and Alexander Löser},
        booktitle = {Proceedings of the 12th International World Wide Web                  Conference},
        month = {May},
        year = 2003,
        url = {http://www.kde.cs.uni-kassel.de/schmitz/publ/www03.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/2710a9b392f9cd7020a886b375e44c678/grahl},
	keywords = {p2p clustering routing}
}

@inproceedings{hotho03ontologies,
        title = {Ontologies improve text document clustering},
        address = {Melbourne, Florida},
        author = {Andreas Hotho and Steffen Staab and Gerd Stumme},
        booktitle = {Proceedings of the 2003 IEEE International Conference on Data Mining},
        month = {November 19-22,},
        pages = {541-544 (Poster},
        publisher = {IEEE {C}omputer {S}ociety},
        year = 2003,
        url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003ontologies.pdf},
        comment = {alpha},
	biburl = {http://www.bibsonomy.org/bibtex/257a39c81cff1982dbefed529be934bee/grahl},
	keywords = {text kdd clustering ontology data-mining}
}

@techreport{hotho03textclustering,
        title = {Text Clustering Based on Background Knowledge},
        author = {Andreas Hotho and Steffen Staab and Gerd Stumme},
        institution = {University of Karlsruhe, Institute AIFB},
        type = {Technical Report },
        volume = 425,
        year = 2003,
        url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003text.pdf},
        comment = {alpha},
	abstract = {Text document clustering plays an important role in providing intuitive
navigation and browsing mechanisms by organizing large amounts of information
into a small number of meaningful clusters. Standard partitional or agglomerative
clustering methods efficiently compute results to this end.
However, the bag of words representation used for these clustering methods is often
unsatisfactory as it ignores relationships between important terms that do not
co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning
has been achieved, because it is only specified extensionally. In order to
deal with the two problems, we integrate background knowledge into the process of
clustering text documents.
First, we preprocess the texts, enriching their representations by background knowledge
provided in a core ontology — in our application Wordnet. Then, we cluster
the documents by a partitional algorithm. Our experimental evaluation on Reuters
newsfeeds compares clustering results with pre-categorizations of news. In the experiments,
improvements of results by background knowledge compared to the baseline
can be shown for many interesting tasks.
Second, the clustering partitions the large number of documents to a relatively small
number of clusters, which may then be analyzed by conceptual clustering. In our approach,
we applied Formal Concept Analysis. Conceptual clustering techniques are
known to be too slow for directly clustering several hundreds of documents, but they
give an intensional account of cluster results. They allow for a concise description
of commonalities and distinctions of different clusters. With background knowledge
they even find abstractions like “food” (vs. specializations like “beef” or “corn”).
Thus, in our approach, partitional clustering reduces first the size of the problem
such that it becomes tractable for conceptual clustering, which then facilitates the
understanding of the results.},
	biburl = {http://www.bibsonomy.org/bibtex/261d58db419af0dbc3681432588219c3d/grahl},
	keywords = {semantic analysis text background web fca concept clustering formal ontology knowledge}
}

@inproceedings{hotho03explaining,
        title = {Explaining Text Clustering Results using  Semantic  Structures},
        address = {Heidelberg},
        author = {Andreas Hotho and Steffen Staab and Gerd Stumme},
        booktitle = {Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases},
        editor = {Nada Lavra\v c and Dragan Gamberger and Hendrik BlockeelLjupco Todorovski},
        pages = {217-228},
        publisher = {Springer},
        series = {LNAI},
        volume = 2838,
        year = 2003,
        url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf},
        comment = {alpha},
	abstract = {Common text clustering techniques offer rather poor capabilities
for explaining to their users why a particular result has been
achieved. They have the disadvantage that they do not relate
semantically nearby terms and that they cannot explain how
resulting clusters are related to each other.
 In this paper, we discuss a way of integrating a large thesaurus
 and the computation of lattices of resulting clusters  into common text clustering
 in order to overcome these two problems.
As its major result, our approach achieves an explanation using an
appropriate level of granularity at the concept level as well as
an appropriate size and complexity of the explaining lattice of
resulting clusters.},
	biburl = {http://www.bibsonomy.org/bibtex/2031e878767fcacab5ba54500eea8e33c/grahl},
	keywords = {semantic fca text ontology formal clustering analysis concept}
}

@inproceedings{hotho02conceptualclustering,
        title = {Conceptual Clustering of Text Clusters},
        author = {A. Hotho and G. Stumme},
        booktitle = {Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002)},
        editor = {G. K\'okai and J. Zeidler},
        pages = {37-45},
        year = 2002,
        url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/FGML02.pdf},
        comment = {alpha},
	biburl = {http://www.bibsonomy.org/bibtex/2e253c44552a046fe90236274bcfeab13/grahl},
	keywords = {fca conceptual analysis clustering concept formal text}
}

@inproceedings{stumme01conceptualclustering,
        title = {Conceptual  Clustering  with  Iceberg  Concept Lattices},
        address = {Universität Dortmund 763},
        author = {G. Stumme and R. Taouil and Y. Bastide and L. Lakhal},
        booktitle = {Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)},
        editor = {R. Klinkenberg and S. Rüping and A. Fick and N. Henze and C. Herzog and R. Molitor and O. Schröder},
        month = {October},
        note = {{P}art of \cite{stumme02computing}},
        year = 2001,
        url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf},
        comment = {alpha},
	biburl = {http://www.bibsonomy.org/bibtex/2f4ec21d5f63dbc213a3a6eae076c4b62/grahl},
	keywords = {knowledge concept closed fca conceptual itemset lattices clustering formal discovery iceberg kdd analysis}
}

@misc{lambiotte05tripartite,
        title = {Collaborative tagging as a tripartite network},
        author = {R. Lambiotte and M. Ausloos},
        month = {Dec},
        year = 2005,
        url = {http://arxiv.org/abs/cs.DS/0512090},
        id = {484851},
	priority = {2},
	eprint = {cs.DS/0512090},
	description = {bibliografica tesi},
	abstract = {We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... <br />To do so, we focus on the correlations between the nodes, depending on their profiles, and use percolation techniques that consist in removing less correlated links and observing the shaping of disconnected islands. The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed.},
	biburl = {http://www.bibsonomy.org/bibtex/265c6f348a54f872fb3e60b4bd64b485b/grahl},
	keywords = {data-mining collaborative-filtering tagging clustering recommender-systems}
}

@inproceedings{begelman2006clustering,
        title = {Automated Tag Clustering:
Improving search and exploration in the tag space},
        author = {Grigory Begelman and Philipp Keller and Frank Smadja},
        booktitle = {Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland},
        year = 2006,
        id = {699842},
	priority = {2},
	description = {bibliografica tesi},
	biburl = {http://www.bibsonomy.org/bibtex/276b741061fab004645c3119db5a17bc3/grahl},
	keywords = {tagging clustering}
}

@inproceedings{comparing2003meila,
        title = {Comparing clusterings },
        author = {Marina Meila},
        booktitle = {Proc. of COLT 03},
        year = 2003,
        url = {http://www.stat.washington.edu/mmp/www.stat.washington.edu/mmp/Papers/compare-colt.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/24cfd500d784db1a78f58e6e42d34d31a/grahl},
	keywords = {clustering evaluation}
}

@article{rand1971,
        title = {Objective criteria for the evaluation of clustering methods},
        author = {W.M. Rand},
        journal = {Journal of the American Statistical Association },
        number = 336,
        pages = {846-850},
        volume = 66,
        year = 1971,
        biburl = {http://www.bibsonomy.org/bibtex/2fd52548cb4bcd8e83dd27e4b55eff1f3/grahl},
	keywords = {criticism evaluation clustering}
}

@book{kaufman1990finding,
        title = {Finding Groups in Data: An Introduction to Cluster Analysis},
        author = {L. Kaufman and P. J. Rousseeuw},
        publisher = {John Wiley},
        year = 1990,
        isbn = {1-58133-109-7},
	biburl = {http://www.bibsonomy.org/bibtex/26f403cbc240f28b3aa461b19aee77238/grahl},
	keywords = {clustering evaluation}
}

@techreport{Weingessel99,
        title = {An examination of indexes for determining the number of clusters in binary data sets},
        author = {A. Weingessel and E. Dimitriadou and S. Dolnicar},
        institution = {SFB ``Adaptive Information Systems and Modeling in Economics and Management Science''},
        number = {Working Paper 29},
        year = 1999,
        url = {http://epub.wu-wien.ac.at/dyn/virlib/wp/showentry?ID=epub-wu-01_188},
        biburl = {http://www.bibsonomy.org/bibtex/28d0a369818293ea71ff632882b988b01/grahl},
	keywords = {evaluation clustering}
}

@book{hoeppner1999fuzzy,
        title = {Fuzzy Cluster Analysis},
        author = {Frank Höppner and Frank Klawonn and Rudolf Kruse and Thomas Runkler},
        publisher = {John Wiley \& Sons, Inc.},
        year = 1999,
        bibsource = {DBLP, http://dblp.uni-trier.de},
	isbn = {3-540-40317-5},
	biburl = {http://www.bibsonomy.org/bibtex/28e77172459a6abf4c50dc14a5e1e0467/grahl},
	keywords = {overview clustering fuzzy evaluation}
}