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One of the most common measures is the Euclidean distance, giving the dissimilarity between the features (m=1,2,..M) of two instances:
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```math
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Distance((x_{i},x_{i'})) = \sum_{m=1}^{M} \Delta_m (x_{im},x_{i'm})
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Distance(x_{i},x_{i'}) = \sum_{m=1}^{M} \Delta_m (x_{im},x_{i'm})
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where:
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Delta_m = (x_{im},x_{i'm})^2
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```
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You can also check:
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[17 types of similarity and dissimilarity measures](https://towardsdatascience.com/17-types-of-similarity-and-dissimilarity-measures-used-in-data-science-3eb914d2681)
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