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Abstract: The clustering problem is well known in the database literature for its numerous applications in problems such as customer segmentation, classification and trend analysis. Unfortunately, all known algorithms tend to break down in high dimensional spaces because of the inherent sparsity of the points. In such high dimensional spaces not all dimensions may be relevant to a given cluster. One way of handling this is to pick the closely correlated dimensions and find clusters in the corresponding... (Update)
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BibTeX entry: (Update)
C. C. Aggarwal, C. Procopiuc, J. L. Wolf, P. S. Yu, and J. S. Park. Fast algorithms for projected clustering. In Proceedings of ACM SIGMOD Conference on Management of Data, pages 61--72, 1999. http://citeseer.ist.psu.edu/aggarwal99fast.html More
@inproceedings{ aggarwal99fast,
author = "Charu C. Aggarwal and Joel L. Wolf and Philip S. Yu and Cecilia Procopiuc and Jong Soo Park",
title = "Fast algorithms for projected clustering",
pages = "61--72",
year = "1999",
url = "citeseer.ist.psu.edu/aggarwal99fast.html" }
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The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://cs.sungshin.ac.kr/~jpark/HOME/refs.html):
Efficient and Effective Clustering Methods for Spatial Data Mining - Ng, Han (1994) (Correct)
BIRCH: An Efficient Data Clustering Method for Very Large .. - Zhang, Ramakrishnan.. (1996) (Correct)
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