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I am working on similarity calculation between entities of similar type. For each entity I am able to make a vector that comprises of multiple vectors itself.

  • A = 50*1 vector
  • B = 100*1 vector
  • C = 50*1 vector
  • D = [age, gender, x-feature, y-feature, z-feature]

Entity = [A B C D], basically concatenating all of those.

My problem is that: since they come from different spaces, each would have different orders of magnitude.
If I run KNN on these vectors, I doubt that results are going to be governed by features of one space only. What should be done to get best KNN results? What "normalisation" would be best here?

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Since you have mixed data, use one-hot encoding for the categorical data so that they become binary data. For the numerical data, normalize them so that their ranges are within [0,1].

Next, you'll have to choose an appropriate distance measure. See the answer here: https://stats.stackexchange.com/questions/218092/how-to-calculate-the-distance-in-knn-for-mixed-data-types

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