class_docstr: A method for determining frequent itemsets in a transactional database. 
  This is done by building a so called FP Growth tree, which can then be mined
  to collect the frequent itemsets. More effective than Apriori for large transactional
  databases.
  
  Parameters:
  -----------
  min_sup: float
      The minimum fraction of transactions an itemets needs to
      occur in to be deemed frequent
class_docstr_tok: ["A","method","for","determining","frequent","itemsets","in","a","transactional","database",".","This","is","done","by","building","a","so","called","FP","Growth","tree",",","which","can","then","be","mined","to","collect","the","frequent","itemsets",".","More","effective","than","Apriori","for","large","transactional","databases",".","Parameters",":","--","--","--","--","--","-","min_sup",":","float","The","minimum","fraction","of","transactions","an","itemets","needs","to","occur","in","to","be","deemed","frequent"]
code: def __init__(self, min_sup=0.3):
      self.min_sup = min_sup
      # The root of the initial FP Growth Tree
      self.tree_root = None
      # Prefixes of itemsets in the FP Growth Tree
      self.prefixes = {}
      self.frequent_itemsets = []
  
file_path: ML-From-Scratch/mlfromscratch/unsupervised_learning/fp_growth.py
imports: from __future__ import division, print_function
  import numpy
  import itertools
label_desc: Use image node_id 1 to create a new FPGrowth object  with example: obj = FPGrowth(min_sup)