****************** Parameter Setting of Curler Algorithm ******************

datafile:           the dataset file, with no class labels, one line for one data.

#cluster:           the number of microclusters.

#dimension:         the dimensionality of the dataset.

#data:              the number of data points.

epsilon_likelihood: probability log likelihood threshold.

epsilon_coshare:    neighborhood co-sharing level threshold (default 0).

microclus file:     the microcluser file of #cluster randomely sampled data

MaxLoopNum:         maximum number of iteration times (2-15 is sufficient). 

basedim:            basedim value of orientation vectors is kept positive (default 0, range [0,#dimension-1]).

       





******************     Output of Curler Algorithm     ******************

output_em.txt: first part:      the original data 

                               (the d dimension values of each data)

               second part:     the microclusters.

                               (the d dimension values of mean point )

                               (one eigenvector of d dimensions)

                               (the index of the microcluster)

 

output_expand.txt:the ordered microclusters, one line for one microcluster

                  ( NND nearest neighbour distance) 

                  ( nearest microcluster neighbour index (may be not consecutive))

                  ( d eigen vectors, each of which has d dimension values )

 	          

membership.txt:  the index of the microcluster to which each data belongs to.







******************     Running Example     ******************

iris dataset:

Step 1 (dos): clustering

Curler iris.txt 150 4 150 0 0 iris_seed150.txt 8 0

Step 2 (matlab): visualization

NNCO(150,4,'output_expand.txt');



image dataset:

Step 1 (dos): clustering

Curler image.txt 500 16 2310 0 0 image_seed500.txt 8 0

Step 2 (matlab): visualization

NNCO(500,16,'output_expand.txt');



cubic dataset:

Step 1 (dos): clustering

Curler cubic.txt 150 2 589 0 0 cubic_seed150.txt 3 0

Step 2 (matlab): visualization

NNCO(150,2,'output_expand.txt');