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بردار خرید
So now you know how to calculate the distance between a purchase vector and a cluster center.
In terms of binary data, like the purchase data, the Manhattan distance between a cluster center and a customer's purchase vector is just the count of the mismatches.
Say you had a couple of two-dimensional binary purchase vectors (1,1) and (1,0).
You can visualize these two purchase vectors in space and see that they have a 45-degree angle between them (see Figure 2.41).
Figure 2.41 An illustration of cosine similarity on two binary purchase vectors
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