Wavelet Toolbox™

Multisignals 1-D clustering

Syntax

• S = mdwtcluster(X)
  S = mdwtcluster(X,'PropName1',PropVal1,'PropName2',PropVal2,...)
  

Description

S = mdwtcluster(X) constructs clusters from a hierarchical cluster tree. The input matrix X is decomposed in row direction using the DWT function with the haar wavelet and the maximum allowed level.

S = mdwtcluster(X,'PropName1',PropVal1,'PropName2',PropVal2,...) allows you to modify some properties. The valid choices for PropName are:

'dirDec'	'r' (row) or 'c' (column). Default value is 'r'.
'level'	Level of the DWT decomposition. Default value is: level=fix(log2(size(X,d))) where d=1 or d=2, depending on the dirDec value.
'wname'	Wavelet name used for DWT. Default value is 'haar'.
'dwtEXTM'	DWT extension mode (see dwtmode).
'pdist'	See Statistics Toolbox™ pdist function. Default value is 'euclidean'.
'linkage'	See Statistics Toolbox™ linkage function. Default value is 'ward'.
'maxclust'	Number of clusters. Default value is 6. The input variable can be a vector.
'lst2clu'	Cell array that contains the list of data to classify. If N is the level of decomposition, the allowed name values for the cells are: 's' -- Signal 'aj' -- Approximation at level j 'dj' -- Detail at level j 'caj' -- Coefficients of approximation at level j 'cdj' -- Coefficients of detail at level j Default value is {'s';'ca1';...;'caN'}.

The output structure S is such that for each partition j:

S.Idx(:,j)	Contains the cluster numbers obtained from the hierarchical cluster tree (see cluster in the Statistics Toolbox™ software).
S.Incons(:,j)	Contains the inconsistent values of each non-leaf node in the hierarchical cluster tree (see Statistics Toolbox™ software function inconsistent).
S.Corr(j)	Contains the cophenetic correlation coefficients of the partition (see Statistics Toolbox™ software function cophenet).

Note    If maxclustVal is a vector, then IdxCLU is a multidimensional array such that IdxCLU(:,j,k) contains the cluster numbers obtained from the hierarchical cluster tree for k clusters.

Examples

• load elecsig10
  lst2clu = {'s','ca1','ca3','ca6'};
  
  % Compute the structure resulting from multisignal clustering
  S = mdwtcluster(signals,'maxclust',4,'lst2clu',lst2clu)
  
  S = 
  
      IdxCLU: [70x4 double]
      Incons: [69x4 double]
        Corr: [0.7920 0.7926 0.7947 0.7631]
  
  % Retrieve indices of clusters
  IdxCLU = S.IdxCLU;
  
  % Plot the first cluster
  plot(signals(IdxCLU(:,1)==1,:)','r');
  hold on; 
  
  % Plot the third clustering
  plot(signals(IdxCLU(:,1)==3,:)','b')
  
  % Check the equality of partitions
  equalPART = isequal(IdxCLU(:,1),IdxCLU(:,3))
  
  equalPART =
  
       1
  
  % So we can see that we obtain the same partitions using
  % coefficents of approximation at level 3 instead of original
  % signals. Much less information is then used.
  

See Also
mdwtdec, wavedec

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