I am using Weka 3.6 to do Association Rule mining. In our data set, each transaction is a word, and each letter in the word is an item. The rules that we are mining would be in the format of {a set of letters} -> {another set of letters}.

So far, I have formatted six transactions by representing the existence of a letter with '1' and the absence of a letter with '0', but this is giving me some unwanted rules. Specifically, I am getting rules where the absence of a letter implies the absence of another letter, e.g. C='0' ==> U='0'.

How do I filter out rules that represent the absence of items?

For what it's worth, here is my .arff file:

@relation transactions
@attribute A {'1','0'}
@attribute C {'1','0'}
@attribute D {'1','0'}
@attribute E {'1','0'}
@attribute I {'1','0'}
@attribute K {'1','0'}
@attribute M {'1','0'}
@attribute N {'1','0'}
@attribute O {'1','0'}
@attribute U {'1','0'}
@attribute Y {'1','0'}
@data
'0','0','0','1','0','1','1','1','1','0','1' %monkey
'0','0','1','1','0','1','0','1','1','0','1' %donkey
'1','0','0','1','0','1','1','0','0','0','0' %make
'0','1','0','0','0','1','1','0','0','1','1' %mucky
'0','1','0','1','1','1','0','1','1','0','0' %conkie
'1','0','0','0','0','1','1','1','1','0','1' %mankoy