Question about (Python/Orange) Apriori associative algorithm I'm trying to wrap my head around Association rules and frequent itemsets.
So I threw my data in, instead of the samples one and sometime it works, sometimes it doesn't.  

rules = Orange.associate.AssociationRulesSparseInducer(data, support = 0.3)

print "%5s   %5s" % ("supp", "conf")
for r in rules:
    print "%5.3f   %5.3f   %s" % (r.support, r.confidence, r)

inducer = Orange.associate.AssociationRulesSparseInducer(support = 0.2, store_examples = True)
itemsets = inducer.get_itemsets(data)
print itemsets
print data.domain
print [data.domain[i].name for i in itemsets[4][0]]


More often than not, itemsets[4][0] shows an IndexError: list index out of range error.
So I start playing around with support = 0.3, support = 0.5, support = 0.2 and itemsets[2][0] or itemsets[3][0].

From the docs:


  support
  Minimal support for the rule. Depending on the data set it should be set to sufficiently high value to avoid running out of working memory (default: 0.3).


True - I tried 0.2 and it quickly blasted my memory on a 800 rows data file.

Any idea what I should do best there or which are viable values for a shopping cart analysis?

800 rows of data (800 orders)
1 to x item(-categories) per order
15 different item-categories in the file, so my data looks like:  

ItemCat1
ItemCat2, ItemCat2, ItemCat2, ItemCat2, ItemCat7, ItemCat7, ItemCat7,     ItemCat7, ItemCat7
ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1,     ItemCat2
ItemCat4, ItemCat4
ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1,     ItemCat1, ItemCat2
ItemCat5
If Python 3 is an option, Orange 3 features FP-growth in its Orange3-Associate add-on:

pip3 install Orange3-Associate


It's well documented: http://orange3-associate.readthedocs.org/

Apriori due to its slowness and resource requirements, as you'd noticed, really isn't an algorithm to consider anymore.