]> BibSonomy publications for /bibtex/1bd3d8e33e8785ecf66408081db016ca4 http://www.bibsonomy.org/burst/bibtex/1bd3d8e33e8785ecf66408081db016ca4 BibSonomy BuRST Feed for /bibtex/1bd3d8e33e8785ecf66408081db016ca4 2008-07-27T03:08:34+02:00 Birch: an efficient data clustering method for very large databases http://www.bibsonomy.org/bibtex/2d6eb981a19f36491d7de5ff0f8249a67/marcoalvarez marcoalvarez 2008-05-14T10:25:13+02:00 Clustering
Birch: an efficient data clustering method for very large databases
Tian Zhang and Raghu Ramakrishnan and Miron Livny ACM SIGMOD Record 25 103--114 (1996)
to Clustering by marcoalvarez on 2008-05-14 10:25:13 ]]> ACM SIGMOD RecordJune2103--114Birch: an efficient data clustering method for very large databases251996Clustering 2008-05-14 10:25:13.0Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely studied problems in this area is the identification of clusters, or densely populated regions, in a multi-dimensional dataset. Prior work does not adequately address the problem of large datasets and minimization of I/O costs.This paper presents a data clustering method named BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrates that it is especially suitable for very large databases. BIRCH incrementally and dynamically clusters incoming multi-dimensional metric data points to try to produce the best quality clustering with the available resources (i.e., available memory and time constraints). BIRCH can typically find a good clustering with a single scan of the data, and improve the quality further with a few additional scans. BIRCH is also the first clustering algorithm proposed in the database area to handle "noise" (data points that are not part of the underlying pattern) effectively.We evaluate BIRCH's time/space efficiency, data input order sensitivity, and clustering quality through several experiments. We also present a performance comparisons of BIRCH versus CLARANS, a clustering method proposed recently for large datasets, and show that BIRCH is consistently superior. BIRCH: an efficient data clustering method for very large databases http://www.bibsonomy.org/bibtex/260f5c714819cecc170523eeb7af4471f/msn msn 2008-03-31T11:26:14+02:00 cites.gradu research.clustering
BIRCH: an efficient data clustering method for very large databases
Tian Zhang and Raghu Ramakrishnan and Miron Livny SIGMOD '96: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data 103-114 (1996)
to cites.gradu research.clustering by msn on 2008-03-31 11:26:14 ]]> SIGMOD '96: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data103-114{BIRCH}: an efficient data clustering method for very large databases1996cites.gradu research.clustering 2008-03-31 11:26:14.0 BIRCH : An efficient data clustering method for very large databases http://www.bibsonomy.org/bibtex/251f2e2f1e3ae42b4e41e5190dd631892/stumme stumme 2008-03-28T12:11:30+01:00 FCA OntologyHandbook
BIRCH : An efficient data clustering method for very large databases
T. Zhang and R. Ramakrishnan and M. Livny Proceedings of the 1996 ACM SIGMOD international conference on Management of Data (SIGMOD'96) 103--114 (1996)
to FCA OntologyHandbook by stumme on 2008-03-28 12:11:30 ]]> Proceedings of the 1996 ACM SIGMOD international conference on Management of Data (SIGMOD'96)June103--114BIRCH : An efficient data clustering method for very large databases1996FCA OntologyHandbook 2008-03-28 12:11:30.0 BIRCH: an efficient data clustering method for very large databases BIRCHhttp://www.bibsonomy.org/bibtex/224266b7284daa37d9998c51a9df3eb17/migake migake 2007-11-08T17:19:50+01:00 imported
BIRCH: an efficient data clustering method for very large databases
Tian Zhang and Raghu Ramakrishnan and Miron Livny SIGMOD '96: Proceedings of the 1996 ACM SIGMOD international conference on Management of data 103--114 (1996)
to imported by migake on 2007-11-08 17:19:50 ]]> New York, NY, USASIGMOD '96: Proceedings of the 1996 ACM SIGMOD international conference on Management of data103--114BIRCH: an efficient data clustering method for very large databases1996imported 2007-11-08 17:19:50.0Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely studied problems in this area is the identification of clusters, or densely populated regions, in a multi-dimensional dataset. Prior work does not adequately address the problem of large datasets and minimization of I/O costs.This paper presents a data clustering method named BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrates that it is especially suitable for very large databases. BIRCH incrementally and dynamically clusters incoming multi-dimensional metric data points to try to produce the best quality clustering with the available resources (i.e., available memory and time constraints). BIRCH can typically find a good clustering with a single scan of the data, and improve the quality further with a few additional scans. BIRCH is also the first clustering algorithm proposed in the database area to handle "noise" (data points that are not part of the underlying pattern) effectively.We evaluate BIRCH's time/space efficiency, data input order sensitivity, and clustering quality through several experiments. We also present a performance comparisons of BIRCH versus CLARANS, a clustering method proposed recently for large datasets, and show that BIRCH is consistently superior. BIRCH: an efficient data clustering method for very large databases BIRCHhttp://www.bibsonomy.org/bibtex/224266b7284daa37d9998c51a9df3eb17/beate beate 2007-11-08T17:19:28+01:00 imported
BIRCH: an efficient data clustering method for very large databases
Tian Zhang and Raghu Ramakrishnan and Miron Livny SIGMOD '96: Proceedings of the 1996 ACM SIGMOD international conference on Management of data 103--114 (1996)
to imported by beate on 2007-11-08 17:19:28 ]]> New York, NY, USASIGMOD '96: Proceedings of the 1996 ACM SIGMOD international conference on Management of data103--114BIRCH: an efficient data clustering method for very large databases1996imported 2007-11-08 17:19:28.0Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely studied problems in this area is the identification of clusters, or densely populated regions, in a multi-dimensional dataset. Prior work does not adequately address the problem of large datasets and minimization of I/O costs.This paper presents a data clustering method named BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrates that it is especially suitable for very large databases. BIRCH incrementally and dynamically clusters incoming multi-dimensional metric data points to try to produce the best quality clustering with the available resources (i.e., available memory and time constraints). BIRCH can typically find a good clustering with a single scan of the data, and improve the quality further with a few additional scans. BIRCH is also the first clustering algorithm proposed in the database area to handle "noise" (data points that are not part of the underlying pattern) effectively.We evaluate BIRCH's time/space efficiency, data input order sensitivity, and clustering quality through several experiments. We also present a performance comparisons of BIRCH versus CLARANS, a clustering method proposed recently for large datasets, and show that BIRCH is consistently superior. BIRCH: an efficient data clustering method for very large databases http://www.bibsonomy.org/bibtex/2d8ede3f66d485d95578bdc3eeda11fc3/jaeschke jaeschke 2007-05-29T18:36:30+02:00 birch clustering databases large tree
BIRCH: an efficient data clustering method for very large databases
Tian Zhang and Raghu Ramakrishnan and Miron Livny Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (SIGMOD'96) 103--114 (1996)
to birch clustering databases large tree by jaeschke on 2007-05-29 18:36:30 ]]> Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (SIGMOD'96)103--114{BIRCH}: an efficient data clustering method for very large databases1996birch clustering databases large tree 2007-05-29 18:36:30.0 BIRCH: An Efficient Data Clustering Method for Very Large Databases. dblphttp://www.bibsonomy.org/bibtex/2a4b8c635a451089724f2634c3f74f1a4/dblp dblp 2006-03-31T00:00:00+02:00 dblp
BIRCH: An Efficient Data Clustering Method for Very Large Databases.
Tian Zhang and Raghu Ramakrishnan and Miron Livny SIGMOD Conference 103-114 (1996)
to dblp by dblp on 2006-03-31 00:00:00 ]]> SIGMOD Conferenceconf/sigmod/96103-114BIRCH: An Efficient Data Clustering Method for Very Large Databases.1996dblp 2006-03-31 00:00:00.0 BIRCH: An Efficient Data Clustering Method for Very Large Databases http://www.bibsonomy.org/bibtex/26c3cb542716983281c80d837fdf86c48/pirot pirot 2006-03-07T21:19:39+01:00 imported
BIRCH: An Efficient Data Clustering Method for Very Large Databases
Tian Zhang and Raghu Ramakrishnan and Miron Livny Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (SIGMOD'96) 103--114 (1996)
to imported by pirot on 2006-03-07 21:19:39 ]]> Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (SIGMOD'96)103--114{BIRCH}: {A}n Efficient Data Clustering Method for Very Large Databases1996imported 2006-03-07 21:19:39.0