[1] |
G. Agrawal.
High-level Interfaces for Data Mining: From Offline Algorithms on
Clusters to Streams on Grids.
In Workshop on Data Mining and Exploration Middleware for
Distributed and Grid Computing, Minneapolis, MN, September 2003. [ bib | ] |
[2] |
S. Bandyopadhyay, C. Gianella, U. Maulik, H. Kargupta, K. Liu, and S. Datta.
Clustering Distributed Data Streams in Peer-to-Peer Environments.
Information Science Journal (In Press), 2004. [ bib | http://www.cs.umbc.edu/~hillol/PUBS/p2pDM.pdf ] |
[3] |
S. Datta, C. Giannella, and H. Kargupta.
K-Means Clustering over a Large, Dynamic Network.
In Proceedings of 2006 SIAM Conference on Data Mining,
Bethesda, MD, April 2006. [ bib | http://www.cs.umbc.edu/~hillol/Kargupta/pubs.html ] |
[4] |
I. Dhillon and D. Modha.
A Data-clustering Algorithm on Distributed Memory Multiprocessors.
In Proceedings of the KDD'99 Workshop on High Performance
Knowledge Discovery, pages 245-260, 1999. [ bib | http://citeseer.nj.nec.com/dhillon00dataclustering.html ] |
[5] |
X. Fern and C. Brodley.
Random Projection for High Dimensional Data Clustering: A Cluster
Ensemble Approach.
In The Twentieth International Conference on Machine Learning
(ICML2003), Washington, DC, August 2003. [ bib | http://mow.ecn.purdue.edu/~xz/rpm_icml03.pdf ] |
[6] |
G. Forman and B. Zhang.
Distributed Data Clustering Can Be Efficient and Exact.
SIGKDD Explorations, 2(2):34-38, 2000. [ bib | http://delivery.acm.org/10.1145/390000/381010/p34-forman.pdf?key1=381010&key2=6909629011&coll=GUIDE&dl=GUIDE&CFID=38766140&CFTOKEN=36394420 ] |
[7] |
D. Foti, D. Lipari, C. Pizzuti, and D. Talia.
Scalable Parallel Clustering for Data Mining on Multicomputers.
In 3rd Workshop on High Performance Data Mining. In conjunction
with International Parallel and Distributed Processing Symposium 2000 (
IPDPS'00 ), Cancun, Mexico, May 2000. [ bib | http://ipdps.eece.unm.edu/2000/datamine/18000391.pdf ] |
[8] |
J. Ghosh, A. Strehl, and S. Merugu.
A Consensus Framework for Integrating Distributed Clusterings Under
Limited Knowledge Sharing.
In Proceedings of NSF Workshop on Next Generation Data
Mining, pages 99-108, Baltimore, MD, November 2002. [ bib | http://www.lans.ece.utexas.edu/~strehl/download/ghosh-ngdm02.pdf ] |
[9] |
A. Gionis, H. Mannila, and P. Tsaparas.
Clustering Aggregation.
In Proceedings of the 21st International Conference on Data
Engineering (ICDE'05), Tokyo, Japan, April 2005. [ bib | http://www.cs.helsinki.fi/u/gionis/papers/icde05.pdf ] |
[10] |
R. L. Grossman, S. Bailey, A. Ramu, B. Malhi, and A. Turinsky.
The Preliminary Design of Papyrus: A System for High Performance,
Distributed Data Mining over Clusters.
In Hillol Kargupta and Philip Chan, editors, Advances in
Distributed and Parallel Knowledge Discovery, pages 259-275. MIT/AAAI
Press, Menlo Park, CA, 2000. [ bib | http://www.rgrossman.com/dl/proc-052.pdf ] |
[11] |
N. Gupta and S. Sen.
Faster Output-sensitive Parallel Algorithms for 3D Convex Hulls and
Vector Maxima.
Journal of Parallel and Distributed Computing,
63(4):488-500, 2003. [ bib | http://portal.acm.org/citation.cfm?id=876693 ] |
[12] |
K. Hammouda and M. Kamel.
HP2PC: Scalable Hierarchically-Distributed Peer-to-Peer Clustering.
In Proceedings of the 2007 SIAM International Conference on Data
Mining (SDM '07), Philadelphia, PA, 2007. [ bib ] |
[13] |
E. Hung and D. Cheung.
Parallel Mining of Outliers in Large Database.
Distributed and Parallel Databases, 12:5-26, July 2002. [ bib | http://www.cs.umd.edu/~ehung/paper/pnl2kluwer.pdf ] |
[14] |
E. Januzaj, H. P. Kriegel, and M. Pfeifle.
Scalable Density Based Distributed Clustering.
In Proceedings of EDBT, volume 2992 of Lecture Notes in
Computer Science, pages 88-105, March 2004. [ bib | http://www.dbs.informatik.uni-muenchen.de/Publikationen/Papers/PKDD04.final.pdf ] |
[15] |
E. Januzaj, H.-P. Kriegel, and M. Pfeifle.
Scalable Density-Based Distributed Clustering.
In The 15th European Conference on Machine Learning (ECML) and
the 8th European Conference on Principles and Practice of Knowledge Discovery
in Databases (PKDD) , Pisa, Italy, September 2004. [ bib | http://www.dbs.informatik.uni-muenchen.de/Publikationen/Papers/PKDD04.final.pdf ] |
[16] |
E. Johnson and H. Kargupta.
Collective, Hierarchical Clustering From Distributed, Heterogeneous
Data.
In M. Zaki and C. Ho, editors, Large-Scale Parallel KDD
Systems. Lecture Notes in Computer Science, volume 1759, pages 221-244.
Springer-Verlag, 1999. [ bib | ] |
[17] |
Pierre-Emmanuel Jouve and Nicolas Nicoloyannis Laboratoire Eric.
A New Method for Combining Partitions, Applications for Cluster
Ensembles in KDD.
In Parallel and Distributed computing for Machine Learning. In
conjunction with the 14th European Conference on Machine Learning (ECML'03)
and 7th European Conference on Principles and Practice of Knowledge Discovery
in Databases (PKDD'03), Cavtat-Dubrovnik, Croatia, September 2003. [ bib | http://paginas.fe.up.pt/~rcamacho/ecml2003/jouve-ecml2003.pdf ] |
[18] |
H. Kargupta, W. Huang, K. Sivakumar, and E. Johnson.
Distributed Clustering Using Collective Principal Component
Analysis.
Knowledge and Information Systems, 3(4):422-448, 2001. [ bib | http://portal.acm.org/citation.cfm?id=545320 ] |
[19] |
M. Klusch, S. Lodi, and G. L. Moro.
Distributed Clustering Based on Sampling Local Density Estimates.
In Proceedings of International Joint Conference on Artificial
Intelligence (IJCAI 2003), pages 485-490, Mexico, August 2003. [ bib | http://www.dfki.de/~klusch/papers/ijcai03-KDEC-paper.pdf ] |
[20] |
H.-P. Kriegel, P. Kröger, A. Pryakhin, and M. Schubert.
Effective and Efficient Distributed Model-based Clustering.
In The Fifth IEEE International Conference on Data Mining
(ICDM'05), Houston, TX, November 2005. [ bib | ] |
[21] |
P. Kunath, H.-P. Kriegel, M. Pfeifle, and M. Renz.
Approximated Clustering of Distributed High-Dimensional Data.
In Proceedings of the Ninth Pacific-Asia Conference on
Knowledge Discovery and Data Mining (PAKDD'05), Hanoi, Vietnam, May 2005. [ bib | ] |
[22] |
A. Lazarevic, D. Pokrajac, and Z. Obradovic.
Distributed Clustering and Local Regression for Knowledge Discovery
in Multiple Spatial Databases.
In Proceedings of 8th European Symposium on Artificial Neural
Networks, pages 129-134, Bruges, Belgium, April 2000. [ bib | http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2000-20.pdf ] |
[23] |
T. Li, S. Zhu, and M. Ogihara.
Algorithms for Clustering High Dimensional and Distributed Data.
Intelligent Data Analysis Journal, 7(4), 2003. [ bib | http://www.cs.rochester.edu/~zsh/pub/ ] |
[24] |
E. Lozano and E. Acuna.
Parallel Algorithms for Distance-based and Density-based Outliers.
In The Fifth IEEE International Conference on Data Mining
(ICDM'05), Houston, TX, November 2005. [ bib | http://academic.uprm.edu/~eacuna/elioedgarieee.pdf ] |
[25] |
S. McClean, B. Scotney, and K. Greer.
Conceptual Clustering Heterogeneous Distributed Databases.
In Workshop on Distributed and Parallel Knowledge Discovery,
Boston, MA, 2000. [ bib | http://www.cs.umbc.edu/~hillol/pkdd2001/papers/McClean.pdf ] |
[26] |
S. McClean, B. Scotney, and F. Palmer.
Conceptual Clustering of Heterogeneous Sequences via Schema
Mapping.
ISMIS 2002, pages 85-93, 2002. [ bib | http://www.epros.ed.ac.uk/mission/papers/papers.html ] |
[27] |
Samer Nassar, Jörg Sander, and Corrine Cheng.
Incremental and Effective Data Summarization for Dynamic
Hierarchical Clustering.
In Proceedings of the 2004 ACM SIGMOD International Conference
on Management of Data, pages 467-478, Paris, France, June 2004. [ bib | http://delivery.acm.org/10.1145/1010000/1007621/p467-nassar.pdf?key1=1007621&key2=1117580901&coll=GUIDE&dl=GUIDE&CFID=24745861&CFTOKEN=85294640 ] |
[28] |
S. Parthasarathy and M. Ogihara.
Clustering Distributed Homogeneous Datasets.
In Proceedings of the Fourth European Conference on Principles
of Data Mining and Knowledge Discovery, volume 1910 of Springer-Verlag
Lecture Notes in Computer Science, pages 566-574, 2000. [ bib | http://portal.acm.org/citation.cfm?id=669679 ] |
[29] |
V. Ramos and J. J. Merelo.
Self-Organized Stigmergic Document Maps: Environment as a
Mechanism for Context Learning, volume 1 of AEB2002 1st Spanish
Conference on Evolutionary and Bio-Inspired Algorithms, chapter 7, pages
284-293.
Centro University de Mérida, Feburary 2002. [ bib | http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_42.html ] |
[30] |
Vitorino Ramos and Ajith Abraham.
Evolving a Stigmergic Self-Organized Data-Mining.
In IADIS, editor, IADIS-04, International Conference on Web
Based Communities, March 2004. [ bib | http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_50.html ] |
[31] |
N. F. Samatova, G. Ostrouchov, A. Geist, and A. Melechko.
RACHET: An Efficient Cover-Based Merging of Clustering Hierarchies
from Distributed Datasets.
Distributed and Parallel Databases, 11(2):157-180, 2002. [ bib | http://portal.acm.org/citation.cfm?id=586459.586462 ] |
[32] |
A. Topchy, A. Jain, and W. Punch.
Combining Multiple Weak Clusterings.
In The Third IEEE International Conference on Data Mining
(ICDM'03), Melbourne, FL, November 2003. [ bib | http://www.cse.msu.edu/prip/Files/topchy_combination.pdf ] |
[33] |
G. Tsoumakas, L. Angelis, and I. Vlahavas.
Clustering Classifiers for Knowledge Discovery from Physically
Distributed Databases.
Data and Knowledge Engineering, 49(3):223-242, June 2004. [ bib | http://portal.acm.org/citation.cfm?id=1011061 ] |
[34] |
S. Vucetic and Z. Obradovic.
Discovering Homogeneous Regions in Spatial Data through
Competition.
In The Seventeenth International Conference on Machine Learning
(ICML2000), Stanford University, CA, June 2000. [ bib | http://portal.acm.org/citation.cfm?id=645529.657789 ] |
[35] |
F.-H. Wang, J.-M. Chang, Y.-L. Wang, and S.-J Huang.
Distributed Algorithms for Finding the Unique Minimum Distance
Dominating Set in Directed Split-stars.
Journal of Parallel and Distributed Computing,
63(4):481-487, 2003. [ bib | http://portal.acm.org/citation.cfm?id=876692 ] |
[36] |
X. Xu, N. Yuruk, Z. Feng, and T. Schweiger.
SCAN: A Structural Clustering Algorithm for Networks.
In Proceedings of the 13th International Conference on Knowledge
Discovery and Data Mining (KDD '07), pages 824-833, New York NY, 2007. [ bib | http://portal.acm.org/citation.cfm?id=1281280 ] |
[37] |
Q. Zhang, J. Liu, and W. Wang.
Approximate clustering on distributed data streams.
In ICDE, pages 1131-1139, 2008. [ bib | http://www.ieeexplore.ieee.org/Xplore/login.jsp?url=/iel5/4492792/4497384/04497522.pdf?tp=&isnumber=4497384&arnumber=4497522 ] |