BIRCH: an efficient data clustering method for very large databases

BIRCH: an efficient data clustering method for very large databases (1996)

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by Tian Zhang , Raghu Ramakrishnan , Miron Livny

Venue:	In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD
Citations:	335 - 2 self

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Datum	Value	Source
TITLE	BIRCH: an efficient data clustering method for very large databases	INFERENCE
AUTHOR NAME	Tian Zhang	SVM HeaderParse 0.2
AUTHOR AFFIL	(“lornputer Sciences Dept. Computer Sciences Dept. Computer Sciences Dept.; [Jniv. of Wisconsin-Maciison [Jniv. of Wisconsin-Maciison LJniv. of Wisconsin-Maclison	SVM HeaderParse 0.2
AUTHOR NAME	Raghu Ramakrishnan	SVM HeaderParse 0.2
AUTHOR AFFIL	(“lornputer Sciences Dept. Computer Sciences Dept. Computer Sciences Dept.; [Jniv. of Wisconsin-Maciison [Jniv. of Wisconsin-Maciison LJniv. of Wisconsin-Maclison	SVM HeaderParse 0.2
AUTHOR NAME	Miron Livny	SVM HeaderParse 0.2
AUTHOR AFFIL	(“lornputer Sciences Dept. Computer Sciences Dept. Computer Sciences Dept.; [Jniv. of Wisconsin-Maciison [Jniv. of Wisconsin-Maciison LJniv. of Wisconsin-Maclison	SVM HeaderParse 0.2
ABSTRACT	Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters, or deusel y populated regions, in a multi-dir nensional clataset. Prior work does not adequately address the problem of large datasets and minimization of 1/0 costs. This paper presents a data clustering method named Bfll (;”H (Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrates that it is especially suitable for very large databases. BIRCH incrementally and clynamicall y 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 goocl clustering with a single scan of the data, and improve the quality further with a few aclditioual scans. BIRCH is also the first clustering algorithm proposerl 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 BIR (;’H versus CLARA NS, a clustering method proposed recently for large datasets, and S11OW that BIRCH is consistently 1	SVM HeaderParse 0.2
YEAR	1996	INFERENCE
VENUE	In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	103--114	INFERENCE
CITATIONS	16 found	ParsCit 1.0