For unsupervised processing, there is a choice of distance function to use for clustering. The options are:

Euclidean |
Traditional Euclidean distance. The distance between two objects will be calculated as the square root of the sum of the squares of the difference in each dimension. |

Manhattan |
The distance between two objects will be calculated as the sum of the absolute value of the difference in each dimension. |

One Class |
A proprietary distance function that works well for highly imbalanced datasets. |

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