期刊
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
卷 44, 期 3, 页码 894-902出版社
AMER CHEMICAL SOC
DOI: 10.1021/ci0342674
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This paper evaluates the use of the fuzzy k-means clustering method for the clustering of files of 2D chemical structures. Simulated property prediction experiments with the Starlist file of logP values demonstrate that use of the fuzzy k-means method can, in some cases, yield results that are superior to those obtained with the conventional k-means method and with Ward's clustering method. Clustering of several small sets of agrochemical compounds demonstrate the ability of the fuzzy k-means method to highlight multicluster membership and to identify outlier compounds, although the former can be difficult to interpret in some cases.
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