How can I use multidimensional matrices as input for clustering in MATLAB?

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SOPAE YI
SOPAE YI 2020-3-12
评论： Ameer Hamza 2020-3-13
Hi all,
I am trying to cluster 2-dimensional time series signals with a similarity measure (maybe corr2).
For example, suppose I have the following signals (1st column is time, 2nd column is amplitude):
A = [1.2,1.5; 2.4,0.5; 3.2,1.5; 4.1,1.0]
B = [1.0,1.0; 2.0,0; 3.0,1.0; 4.0,0.5]
C = [1.1,1.2; 2.2,1.3; 3.3,1.5; 4.2,1.7]
D = [1.3,1.3; 2.1,1.4; 3.2,1.7; 4.3, 2.0]
E = [1.3,1.8; 2.3,0.4; 3.1,1.6; 4.3,0.8]
After applying a clustering technique, such as K-means (hard partition) or Fuzzy (soft partition) or something else, I am expecting 2 clusters like this:
Cluster 1 (A, B, E: see figure 1) and cluster 2 (C, D: see figure 2)
Plot of A, B, E
Plot of C, D
I have been searching for a way to cluster 2-dimensional matrices but I have not found any MATLAB code I can adopt to cluster my matrices.
How can I use multidimensional matrices like A, B, C, D and E as input for clustering in MATLAB?

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Ameer Hamza
Ameer Hamza 2020-3-12
You can first convert each matrix to a linear array and then apply a clustering algorithm. Since the corresponding element of each matrix have the same unit, so the shape and order of input do not matter to the clustering algorithm. For example,
A = [1.2,1.5; 2.4,0.5; 3.2,1.5; 4.1,1.0];
B = [1.0,1.0; 2.0,0; 3.0,1.0; 4.0,0.5];
C = [1.1,1.2; 2.2,1.3; 3.3,1.5; 4.2,1.7];
D = [1.3,1.3; 2.1,1.4; 3.2,1.7; 4.3, 2.0];
E = [1.3,1.8; 2.3,0.4; 3.1,1.6; 4.3,0.8];
M = [A(:) B(:) C(:) D(:) E(:)]'; % each matrix A,B,...,E is now a row of matrix M
idx = kmeans(M, 2, 'Distance', 'correlation')
result:
idx =
1
1
2
2
1
It identified 1st, 2nd, and 5th row are one cluster, 3rd and fourth are other cluster.
  2 个评论
SOPAE YI
SOPAE YI 2020-3-12
编辑：SOPAE YI 2020-3-13
Hi Mr. Hamza,
Your answer is AWESOME!!
Thanks a million!!
I have learned something :--)
One more question,
Since most of my data have different lengths…
Can we cluster matrices of different sizes like following?
A = [1.2,1.5; 2.4,0.5; 3.2,1.5];
B = [1.0,1.0; 2.0,0; 3.0,1.0; 4.0,0.5];
C = [1.1,1.2; 2.2,1.3; 3.3,1.5];
D = [1.3,1.3; 2.1,1.4; 3.2,1.7; 4.3,2.0; 5.0,1.9];
E = [1.3,1.8; 2.3,0.4; 3.1,1.6; 4.3,0.8];
*** I noticed that ‘interp1’ is losing ‘time information’. The time (1st column) of each point is important in my research. I’d like to keep the time information of all matrices.
I do not have to keep the actual time values, I just want to keep the proportional time interval between points (points within 1st column). (e.g. Z = [1, 1.2; 6, 2.1; 8, 3.4 ] can be rewritten as ZZ = [1, 1.2; 4, 2.1; 5, 3.4 ] ). As they still have the same ratio (3 to 1) of time intervals, they have the same time information.
Ameer Hamza
Ameer Hamza 2020-3-13
The clustering algorithm must take data with a consistent dimension, so I am not sure is there an easy way to apply clustering in a situation where the size of input matrices is not equal. Please refer to my comment on your other question to see how you can avoid losing information in matrix B.

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