Clustering Methods for Large Databases: From the Past to the Future
Summary: Survey of clustering methods for large databases, with high-dimensional vectors and multimedia objects. Reviews index-based, preprocessing, and DB/ML hybrids enabling scalable clustering, linking classic techniques to future directions. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Alexander Hinneburg (University of Halle)
- 2. Daniel A. Keim (University of Halle)
BibTeX Citation
@inproceedings{hinneburg_sigmod99,
title = {{Clustering Methods for Large Databases: From the Past to the Future}},
author = {Hinneburg, Alexander and Keim, Daniel A.},
series = {{SIGMOD} '99},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/304182.304232},
url = {https://dl.acm.org/doi/10.1145/304182.304232},
year = {1999}
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