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Multivac
Multivac
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In an academic paperpaper, they talk about using a nearest neighbour algorithm to predict the cluster of a new point. And how the number of nearest neighbours is set to 10 in their example. What do they mean with this? The two things I could think of were:

  1. Look which 10 points used in the training set (neighbours) are closest and then assign it to the cluster of which the majority of the points come from.

    Look which 10 points used in the training set (neighbours) are closest and then assign it to the cluster of which the majority of the points come from.

  2. Collect one by one the closest points from the training set until you have 10 points that come from one single cluster. That is the cluster to which the point belongs.

    Collect one by one the closest points from the training set until you have 10 points that come from one single cluster. That is the cluster to which the point belongs.

What are the other ways to assign a(n) (existing) cluster to a new point?

In an academic paper, they talk about using a nearest neighbour algorithm to predict the cluster of a new point. And how the number of nearest neighbours is set to 10 in their example. What do they mean with this? The two things I could think of were:

  1. Look which 10 points used in the training set (neighbours) are closest and then assign it to the cluster of which the majority of the points come from.
  2. Collect one by one the closest points from the training set until you have 10 points that come from one single cluster. That is the cluster to which the point belongs.

What are the other ways to assign a(n) (existing) cluster to a new point?

In an academic paper, they talk about using a nearest neighbour algorithm to predict the cluster of a new point. And how the number of nearest neighbours is set to 10 in their example. What do they mean with this? The two things I could think of were:

  1. Look which 10 points used in the training set (neighbours) are closest and then assign it to the cluster of which the majority of the points come from.

  2. Collect one by one the closest points from the training set until you have 10 points that come from one single cluster. That is the cluster to which the point belongs.

What are the other ways to assign a(n) (existing) cluster to a new point?

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strateeg32
strateeg32
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How to use spectral clustering to predict?

In an academic paper, they talk about using a nearest neighbour algorithm to predict the cluster of a new point. And how the number of nearest neighbours is set to 10 in their example. What do they mean with this? The two things I could think of were:

  1. Look which 10 points used in the training set (neighbours) are closest and then assign it to the cluster of which the majority of the points come from.
  2. Collect one by one the closest points from the training set until you have 10 points that come from one single cluster. That is the cluster to which the point belongs.

What are the other ways to assign a(n) (existing) cluster to a new point?