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?


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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