CoFD : An algorithm for non-distance based clustering in high dimensional spaces. 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 non-distance based clustering algorithm for high dimensional spaces. Based on the maximum likelihood principle, CoFD is to optimize parameters to maximize the likelihood between data points and the model generated by the parameters. Experimental results on both synthetic data sets and a real data set show the efficiency and effectiveness of CoFD.
References in zbMATH (referenced in 2 articles , 1 standard article )
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- Li, Tao; Zhu, Shenghuo; Ogihara, Mitsunori: Algorithms for clustering high dimensional and distributed data (2003)
- Zhu, Shenghuo; Li, Tao; Ogihara, Mitsuonri: CoFD : An algorithm for non-distance based clustering in high dimensional spaces (2002)