Arthur, D. & Vassilvitskii, S.
(2007):
k-means++: the advantages of careful seeding.
In: SODA '07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms,
Philadelphia, PA, USA.
[BibTeX][Endnote]
@inproceedings{1283494,
author = {Arthur, David and Vassilvitskii, Sergei},
title = {k-means++: the advantages of careful seeding},
booktitle = {SODA '07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms},
publisher = {Society for Industrial and Applied Mathematics},
address = {Philadelphia, PA, USA},
year = {2007},
pages = {1027--1035},
isbn = {978-0-898716-24-5},
keywords = {algorithm, careful, clustering, inex08paper, kmeans, kmeans++, paper, seeding}
}
%0 = inproceedings
%A = Arthur, David and Vassilvitskii, Sergei
%B = SODA '07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
%C = Philadelphia, PA, USA
%D = 2007
%I = Society for Industrial and Applied Mathematics
%T = k-means++: the advantages of careful seeding
Falkowski, T.; Barth, A. & Spiliopoulou, M.
(2007):
DENGRAPH: A Density-based Community Detection Algorithm.
In: In Proc. of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence,,
[Volltext]
[BibTeX][Endnote]
@inproceedings{FalBarSpi07,
author = {Falkowski, Tanja and Barth, Anja and Spiliopoulou, Myra},
title = {DENGRAPH: A Density-based Community Detection Algorithm},
booktitle = {In Proc. of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence,},
year = {2007},
pages = {112-115},
url = {http://wwwiti.cs.uni-magdeburg.de/~tfalkows/publ/2007/WI_FalBarSpi07.pdf},
keywords = {algorithm, based, clustering, community, density, detection}
}
%0 = inproceedings
%A = Falkowski, Tanja and Barth, Anja and Spiliopoulou, Myra
%B = In Proc. of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence,
%D = 2007
%T = DENGRAPH: A Density-based Community Detection Algorithm
%U = http://wwwiti.cs.uni-magdeburg.de/~tfalkows/publ/2007/WI_FalBarSpi07.pdf
Brandes, U.; Gaertler, M. & Wagner, D.
(2003):
Experiments on graph clustering algorithms.
In: Lecture notes in computer science,
Verlag/Publisher: Springer.
Erscheinungsjahr/Year: 2003.
Seiten/Pages: 568-579.
[Volltext] [BibTeX]
[Endnote]
@article{brandes2003experiments,
author = {Brandes, U. and Gaertler, M. and Wagner, D.},
title = {Experiments on graph clustering algorithms},
journal = {Lecture notes in computer science},
publisher = {Springer},
year = {2003},
pages = {568--579},
url = {http://scholar.google.de/scholar.bib?q=info:gDNQfOoSm6cJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=2},
keywords = {algorithm, clustering, community, detection, evaluation, graph}
}
%0 = article
%A = Brandes, U. and Gaertler, M. and Wagner, D.
%D = 2003
%I = Springer
%T = Experiments on graph clustering algorithms
%U = http://scholar.google.de/scholar.bib?q=info:gDNQfOoSm6cJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=2
Newman, M. E. J.
(2003):
The structure and function of complex networks.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
Inspired by empirical studies of networked systems such as the Internet,
cial networks, and biological networks, researchers have in recent years
veloped a variety of techniques and models to help us understand or predict
e behavior of these systems. Here we review developments in this field,
cluding such concepts as the small-world effect, degree distributions,
ustering, network correlations, random graph models, models of network growth
d preferential attachment, and dynamical processes taking place on networks.
@misc{citeulike:155,
author = {Newman, M. E. J.},
title = {The structure and function of complex networks},
year = {2003},
url = {http://arxiv.org/abs/cond-mat/0303516},
keywords = {algorithm, clustering, complex_systems, folksonomy, information, kdubiq, network, retrieval, scale_free_networks, small, socialnetwork, summerschool, theory, web, web_graph, world},
abstract = {Inspired by empirical studies of networked systems such as the Internet,
social networks, and biological networks, researchers have in recent years
developed a variety of techniques and models to help us understand or predict
the behavior of these systems. Here we review developments in this field,
including such concepts as the small-world effect, degree distributions,
clustering, network correlations, random graph models, models of network growth
and preferential attachment, and dynamical processes taking place on networks.}
}
%0 = misc
%A = Newman, M. E. J.
%B = }
%C =
%D = 2003
%I =
%T = The structure and function of complex networks}
%U = http://arxiv.org/abs/cond-mat/0303516
Newman, M.
(2003):
Fast algorithm for detecting community structure in networks.
In: Physical Review E,
Vol. 69,
Erscheinungsjahr/Year: 2003.
[Volltext] [BibTeX]
[Endnote]
@article{newman03fast,
author = {Newman, M.E.J.},
title = {Fast algorithm for detecting community structure in networks},
journal = {Physical Review E},
year = {2003},
volume = {69},
url = {http://arxiv.org/abs/cond-mat/0309508},
keywords = {algorithm, clustering, community, fast, networks}
}
%0 = article
%A = Newman, M.E.J.
%D = 2003
%T = Fast algorithm for detecting community structure in networks
%U = http://arxiv.org/abs/cond-mat/0309508
Newman, M.
(2003):
Fast algorithm for detecting community structure in networks.
In: Physical Review E,
Vol. 69,
Erscheinungsjahr/Year: 2003.
[Volltext] [BibTeX]
[Endnote]
@article{newman03fast,
author = {Newman, M.E.J.},
title = {Fast algorithm for detecting community structure in networks},
journal = {Physical Review E},
year = {2003},
volume = {69},
url = {http://arxiv.org/abs/cond-mat/0309508},
keywords = {algorithm, clustering, community, gn, modularity, network, social}
}
%0 = article
%A = Newman, M.E.J.
%D = 2003
%T = Fast algorithm for detecting community structure in networks
%U = http://arxiv.org/abs/cond-mat/0309508
Wang, H.; 0010, W. W.; Yang, J. & Yu, P. S.
(2002):
Clustering by pattern similarity in large data sets..
In: SIGMOD Conference,
[Volltext]
[BibTeX][Endnote]
@inproceedings{conf/sigmod/WangWYY02,
author = {Wang, Haixun and 0010, Wei Wang and Yang, Jiong and Yu, Philip S.},
title = {Clustering by pattern similarity in large data sets.},
editor = {Franklin, Michael J. and Moon, Bongki and Ailamaki, Anastassia},
booktitle = {SIGMOD Conference},
publisher = {ACM},
year = {2002},
pages = {394-405},
url = {http://dblp.uni-trier.de/db/conf/sigmod/sigmod2002.html#WangWYY02},
isbn = {1-58113-497-5},
keywords = {algorithm, clustering, data, fca?, large, pClusters, paper, pattern}
}
%0 = inproceedings
%A = Wang, Haixun and 0010, Wei Wang and Yang, Jiong and Yu, Philip S.
%B = SIGMOD Conference
%D = 2002
%I = ACM
%T = Clustering by pattern similarity in large data sets.
%U = http://dblp.uni-trier.de/db/conf/sigmod/sigmod2002.html#WangWYY02
Robardet, C. & Feschet, F.
(2000):
A New Methodology to Compare Clustering Algorithms.
In: IDEAL '00: Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents,
London, UK.
[BibTeX][Endnote]
@inproceedings{686448,
author = {Robardet, Celine and Feschet, Fabien},
title = {A New Methodology to Compare Clustering Algorithms},
booktitle = {IDEAL '00: Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents},
publisher = {Springer-Verlag},
address = {London, UK},
year = {2000},
pages = {565--570},
isbn = {3-540-41450-9},
keywords = {clustering, comparison, algorithm}
}
%0 = inproceedings
%A = Robardet, Celine and Feschet, Fabien
%B = IDEAL '00: Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
%C = London, UK
%D = 2000
%I = Springer-Verlag
%T = A New Methodology to Compare Clustering Algorithms
Ester, M.; Kriegel, H.-P.; Sander, Jö. & Xu, X.
(1996):
A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise.
In: Proc. of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96),
[BibTeX][Endnote]
@inproceedings{Ester1996,
author = {Ester, Martin and Kriegel, Hans-Peter and Sander, Jörg and Xu, Xiaowei},
title = {A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise},
booktitle = {Proc. of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96)},
year = {1996},
pages = {226-231},
keywords = {algorithm, based, clustering, density}
}
%0 = inproceedings
%A = Ester, Martin and Kriegel, Hans-Peter and Sander, Jörg and Xu, Xiaowei
%B = Proc. of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96)
%D = 1996
%T = A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
MacQueen, J.
(1967):
Some Methods for Classification and Analysis of Multivariate Observations.
In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability - Vol. 1,
[Volltext]
[BibTeX][Endnote]
@inproceedings{mcqueen1967smc,
author = {MacQueen, J.},
title = {Some Methods for Classification and Analysis of Multivariate Observations},
editor = {Le Cam, L. M. and Neyman, J.},
booktitle = {Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability - Vol. 1},
publisher = {University of California Press, Berkeley, CA, USA},
year = {1967},
pages = {281--297},
url = {http://projecteuclid.org/euclid.bsmsp/1200512992},
keywords = {algorithm, clustering, k-means}
}
%0 = inproceedings
%A = MacQueen, J.
%B = Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability - Vol. 1
%D = 1967
%I = University of California Press, Berkeley, CA, USA
%T = Some Methods for Classification and Analysis of Multivariate Observations
%U = http://projecteuclid.org/euclid.bsmsp/1200512992