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Weka is a library of machine learning algorithms for data mining tasks (data mining). It also includes a powerful graphical environment useful.

For lexical acquisition project I'm working on needed to run weka from command line, as there has been direct to find out how to do I make a summary.

The idea is that we must choose an algorithm, train, save the model generated from this training and use it to classify the data we want.

Training: Once chosen the algorithm (in our case we use a decision tree
weka.classifiers.trees.J48
1. ) model created by training the tree with the training data:
   
   1. java-Xmx [MEGABYTES_DE_MEM_PARA_LA_TAREA] M-cp [PATH_A_weka.jar] [ALGORITMO_DE_APRENDIZAJE + PARAMETERS]-t [FICHERO_ENTRENAMIENTO]-d [FICHERO_DONDE_SE_GUARDA_EL_MODELO]
      For example: java-

        
       Xmx1024M-cp / opt/weka-3-4-9/weka.jar weka.classifiers.trees.J48 -C 0.25-M 2-t-d rweka.model 
       rweka.arff  

      
      
   Rating: With the model generated in the previous step for sorting data and execute:
   

     java-Xmx [MEGABYTES_DE_MEM_PARA_LA_TAREA] M -cp [PATH_A_weka.jar] [ALGORITMO_DE_APRENDIZAJE]-l [FICHERO_CON_EL_MODELO]-T [FICHERO_A_CLASIFICAR]-p 0  

   
   In my case:
   Xmx1024M-java-cp / opt/weka-3-4-9/weka . jar weka.classifiers.trees.J48 Rweka.model-T-l-p 0
   rweka.arff

     This step will return to the standard output the classification of each element of the file in a line with the number of this element first, followed by the class assigned algorithm and additional information: 
    [NÚMERO_ELEMENTO] [CLASE_ASIGNADA] [CONFIANZA_EN_LA_RESPUESTA] [CLASE_REAL]  

   Notes:
   
Files with the training data and classification must be in ARFF format

2. .
   
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