@article{d24c901b69964f5a9252a1260a47951d,
title = "Erratum",
author = "Jurewicz, {Amy J.G.} and Watson, {E. Bruce}",
note = "Funding Information: can be contrasted with a standard clustering algorithm, like K-means 141, that separates data into clusters by minimizing data distances from cluster centers, a criterion which yields very simple cluster boundaries. Applying K-means to our data set with K = 2 we obtain the results shown in Figure 7. Obviously this clustering algorithm is less suitable to this problem than ours, because it does not reflect the intuitive notion that proximity between points within a cluster should be a key criterion. This highlights the flexibility of the SV-algorithm, which takes proximity into account and can represent clusters with arbitrary shapes. Acknowledgments. This work was partially supported by the Israel Ministry of Science.",
year = "1989",
month = jun,
doi = "10.1007/BF00375346",
language = "English (US)",
volume = "102",
pages = "255",
journal = "Contributions to Mineralogy and Petrology",
issn = "0010-7999",
publisher = "Springer Verlag",
number = "2",
}