title: Is it possible using numeric attribute as class for K-means clustering?
body: <pre><code>@attribute CustomerID       NUMERIC
  @attribute Age              {A,B,C,D,E,F,G,H,I,J,K}
  @attribute Region           {A,B,C,D,E,F,G,H}
  @attribute ProductSubClass  NUMERIC
  @attribute ProductID        NUMERIC 
  @attribute Quantity         NUMERIC
  @attribute Cost             NUMERIC
  @attribute sales            NUMERIC
  
  @data
  00141833,F,F,130207,4710105011011,2,44,52
  01376753,E,E,110217,4710265849066,1,150,129
  01603071,E,G,100201,4712019100607,1,35,39
  01738667,E,F,530105,4710168702901,1,94,119
  </code></pre>
  
  <p>above is header and a protion of trianing dataset training.arff file 
  I want to use Kmeans clustering  and J48 classifier, and I can do it without any problems. and flowing is my test dataset test.arff</p>
  
  <pre><code>@attribute CustomerID       NUMERIC
  @attribute Age              {A,B,C,D,E,F,G,H,I,J,K}
  @attribute Region           {A,B,C,D,E,F,G,H}
  @attribute ProductSubClass  NUMERIC
  @attribute ProductID        INTEGER
  @attribute Quantity         NUMERIC
  @attribute Cost             NUMERIC
  @attribute sales            NUMERIC
  
  @data
  1754698,H,A,560402,?,1,676,849
  1027365,F,C,530404,?,1,170,219
  956710,E,E,500303,?,1,36,59
  </code></pre>
  
  <p>In both case I ensured ProductID is selected as Class</p>
  
  <p>here is the steps I did</p>
  
  <pre><code>Setp1: assigning "AddCluster" to use K-means clusterig for each instance in the dataset 
  step2: and then using J48 classificaion algorithm to evaluate the performance of the clustering algorithms using 10-fold cross validation option 
  Step3: save Finalized Model and close weka (I am closing to test if I can relode and use it agian)
  Step4:Load the Model in weaka (Useing "Load Model")
  step5: This time I am selecting "supplied test set"  and select test file to predict (which is same formate as I mentioned in the questien above)
  step6: I am trying "Re-evaluate model on  current test set" 
  </code></pre>
  
  <p>But here I am getting a notificaion "Data used to train  mode test set are not compatible.would you like to automiatically wrap the classifier
  in an "inputMappedClassifier before proceeding ?"" If I click "NO" it shows "Train and test set are not compatible ... 5 != 6" and if "YES" it
  gives following output inthe plainText:</p>
  
  <pre><code>=== Predictions on user test set ===
  
      inst#     actual  predicted error prediction
          1          ?      0              ? 
          2          ?      0              ? 
          3          ?      0              ? 
          4          ?      0              ? 
          5          ?      0              ? 
          6          ?      0              ? 
          7          ?      0              ? 
          8          ?      0              ? 
          9          ?      0              ? 
         10          ?      0              ? 
         11          ?      0              ? 
         12          ?      0              ? 
         13          ?      0              ? 
         14          ?      0              ? 
         15          ?      0              ? 
         16          ?      1              ? 
         17          ?      0              ? 
         18          ?      0              ? 
         19          ?      0              ? 
         20          ?      0              ? 
         21          ?      0              ? 
  </code></pre>
  
  <p>Now 
  1. Is it possible to Using Numeric field ProductID as a class because I have to predict customer choice of product based on ProductID under consideration other attributes. </p>
  
  <ol start="2">
  <li>If So, I am encountered another problem <em>Train and test set are not compatible</em> is there any connection of this error to choosing Numeric attribute ?</li>
  </ol>
  
  <p>NOTE: I am using Weka 3.8.1  GUI</p>