Finding Generalized Projected Clusters in High Dimensional Spaces
Summary: Generalized projected clustering finds clusters in arbitrarily aligned subspaces, subspaces defined per cluster, not from original attributes. Uses extended feature vectors to scale to large databases; provides tunable time/space–accuracy tradeoffs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Charu C. Aggarwal (IBM)
- 2. Philip S. Yu (IBM)
BibTeX Citation
@inproceedings{aggarwal_sigmod00,
title = {{Finding Generalized Projected Clusters in High Dimensional Spaces}},
author = {Aggarwal, Charu C. and Yu, Philip S.},
series = {{SIGMOD} '00},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/342009.335383},
url = {https://dl.acm.org/doi/10.1145/342009.335383},
year = {2000}
}
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