CODEWITHSUNDEEP
rmultidimensional-arrayk-means
AnonyMN
AnonyMN

Reputation: 1

k means clustering on matrix

I am trying to cluster a Multidimensional Functional Object with the "kmeans" algorithms. What does it mean: So I don't have anymore a vector per each row or Individual, even more a 3x3 observation matrix per each Individual.For example: Individual = 1 has the following observations:

(x1, x2, x3),(y1,y2,y3),(z1,z2,z3).

The same structure of observations is also given for the other Individuals. So do you know how I can cluster with "kmeans" including all 3 observation vectors -and not only one observation vector how it is normal used for "kmeans" clustering? Would you do it for each observation vector, f.e. (x1, x2, x3), separately and then combine the Information somehow together? I want to do this with the kmeans() Function in R.

Many thanks for your answers!

Upvotes: 0

Views: 1518

Answers (2)

Kalees Waran
Kalees Waran

Reputation: 659

Just give the argument to kmeans() with all the three columns it'll calculate the distances in 3 dimension, if that is what you are looking for.

Upvotes: 0

MaxPowers
MaxPowers

Reputation: 5486

Using k-means you interpret each observation as a point in an N-dimensional vector space. Then you minimize the distances between your observations and the cluster centers.

Since, the data is viewed as dots in an N-dim space, the actual arrangement of the values does not matter.

You can, therefore, either tell your k-means routine to use a matrix norm, for example the Frobenius norm, to compute the distances. The other way would be to flatten your observations from 3 by 3 matrices to 1 by 9 vectors. The Frobenius norm of a NxN matrix is equivalent to the euclidean norm of a 1xN^2 vector.

Upvotes: 1

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