How can we use clustering results in weka ? -

i using weka internship have little knowledge data mining. so, maybe knows how can apply following results on data-sets data cluster ? method use compute distances between attributes , mean value of each cluster classify them nearest value. method rough me .

=== run information ===  scheme:weka.clusterers.em -i 100 -n -1 -m 1.0e-6 -s 100 relation:     wcet_cluster6 - copie-weka.filters.unsupervised.attribute.remove-r1-3,5-weka.filters.unsupervised.attribute.remove-r5-12 instances:    467 attributes:   4               max               alt               stmt               bb test mode:evaluate on training data  === model , evaluation on training set ===  em  number of clusters selected cross validation: 6                cluster attribute          0        1        2        3        4        5               (0.28)   (0.11)   (0.25)   (0.16)   (0.04)   (0.17) ================================================================== max   mean         9.0148  10.9112  11.2826  10.4329  11.2039  10.0546   std. dev.    1.8418   2.7775   3.0263   2.5743   2.2014   2.4614  alt   mean         0.0003  19.6467   0.4867   2.4565   44.191   8.0635   std. dev.    0.0175   5.7685   0.5034   1.3647  10.4761   3.3021  stmt   mean         0.7295  77.0348   3.2439  12.3971 140.9367  33.9686   std. dev.    1.0174  21.5897   2.3642   5.1584  34.8366  11.5868  bb   mean         0.4362  53.9947   1.4895   7.2547 114.7113  22.2687   std. dev.    0.5153  13.1614   0.9276   3.5122  28.0919   7.6968    time taken build model (full training data) : 4.24 seconds  === model , evaluation on training set ===  clustered instances  0      163 ( 35%) 1       50 ( 11%) 2       85 ( 18%) 3       73 ( 16%) 4       18 (  4%) 5       78 ( 17%)   log likelihood: -9.09081 

thanks help!!

i think no-one can answer this. tips off top of head.

you have used em clustering algorithm, see animated gif on wikipedia page. weka's documentation synopsis:

"em assigns probability distribution each instance indicates probability of belonging each of clusters. "

is complex output want? selects number of clusters (unless constrain number).

in weka 3.7 can use unsupervised attribute filter "clustermembership" in preprocess dialog replace dataset result of cluster assignments. need select 1 reference attribute, though. default selects last one. creates hard-to -interpret output.