code: function(obj) {
           
           this.logger = Logger.getLogger("FPGrowth_js");
          var jvmObject;
          if (obj instanceof org.apache.spark.mllib.fpm.FPGrowth) {
              jvmObject = obj;
          } else {
              jvmObject = new org.apache.spark.mllib.fpm.FPGrowth();
          }
           JavaWrapper.call(this, jvmObject);
  
      }
docs: A parallel FP-growth algorithm to mine frequent itemsets. The algorithm is described in
  [[http://dx.doi.org/10.1145/1454008.1454027 Li et al., PFP: Parallel FP-Growth for Query
  Recommendation]]. PFP distributes computation in such a way that each worker executes an
  independent group of mining tasks. The FP-Growth algorithm is described in
  [[http://dx.doi.org/10.1145/335191.335372 Han et al., Mining frequent patterns without candidate
  generation]].
  
  @param minSupport the minimal support level of the frequent pattern, any pattern appears
  more than (minSupport * size-of-the-dataset) times will be output
  @param numPartitions number of partitions used by parallel FP-growth
  
  @see [[http://en.wikipedia.org/wiki/Association_rule_learning Association rule learning
  (Wikipedia)]]
  
  @classdesc
   
  Constructs a default instance with default parameters {minSupport: `0.3`, numPartitions: same
  as the input data}.
  
  @class
  @memberof module:eclairjs/mllib/fpm