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Abstract: The clustering problem, which aims at identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity clusters, has been widely studied. Traditional clustering algorithms use distance functions to measure similarity and are not suitable for high dimensional spaces. In this paper, we propose CoFD algorithm, which is a nondistance based clustering algorithm for high dimensional spaces. (Update)
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BibTeX entry: (Update)
Shenghuo Zhu, Tao Li, and Mitsunori Ogihara. CoFD: An algorithm for non-distance based clustering in high dimensional spaces. In Proceedings of 4th International Conference on Data Warehousing and Knowledge Discovery(DaWaK 2002. http://citeseer.ist.psu.edu/zhu02cofd.html More
@misc{ zhu02cofd,
author = "S. Zhu and T. Li and M. Ogihara",
title = "CoFD: An algorithm for non-distance based clustering in high dimensional
spaces",
text = "Shenghuo Zhu, Tao Li, and Mitsunori Ogihara. CoFD: An algorithm for non-distance
based clustering in high dimensional spaces. In Proceedings of 4th International
Conference on Data Warehousing and Knowledge Discovery(DaWaK 2002.",
year = "2002",
url = "citeseer.ist.psu.edu/zhu02cofd.html" }
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