BIRCH: an efficient data clustering method for very large databases

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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:	576 - 2 self

BibTeX

@INPROCEEDINGS{Zhang96birch:an,
    author = {Tian Zhang and Raghu Ramakrishnan and Miron Livny},
    title = {BIRCH: an efficient data clustering method for very large databases},
    booktitle = {In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD},
    year = {1996},
    pages = {103--114}
}

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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

Keyphrases

large database large datasets efficient data database area single scan considerable interest balanced iterative reducing birch time space efficiency underlying pattern data clustering method versus clara n multi-dimensional metric data point performance comparison multi-dir nensional clataset populated region udied problem useful pattern available memory first clustering algorithm proposerl available resource several experiment data point prior work time constraint data input order sensitivity aclditioual scan

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