Apriori Algorithm Implementation

I am using an apiori algorithm implementation to generate association rules from a transaction set and I am getting the following association rules. but I get an association rules 1->8 can i assume 开发者_高级运维8->1 because see the association rules it starts from 0 and ends till 9 because there are 10 product classes, but using this algorithm I am not getting something like 8->2 or 9->1, so can i reverse an association rules 2->8 to 8->2. if not can someone point to a better apiori algorithm implementation

0-->5
0-->9
1-->2
1-->4
1-->5
1-->7
1-->8
1-->9
2-->3
2-->4
2-->5
2-->6
2-->7
2-->8
2-->9
3-->4
3-->5
3-->6
3-->7
3-->8
4-->5
4-->6
4-->7
4-->8
4-->9
5-->6
5-->7
5-->8
5-->9
6-->7
6-->8
6-->9
7-->8
7-->9
8-->9

You can get my favourite apriori implementation here:

http://www.borgelt.net/apriori.html

(Christian Borgelt also has implementations for many other mining algorithms.)

I use it regularly to mine datasets with millions of entries and it's blazingly fast. And you can configure it to do what you want (frequent item sets vs. association rules).

of course you can assume so (1=>9 is equal to 9=>1). the items are basically combination among the others, not permutation. FPGrowth is way more efficient than Apriori

If you want to download a Java version of Apriori and other algorithms for frequent itemset mining, you can check my website:

http://www.philippe-fournier-viger.com/spmf/

It also offers implementations of Eclat, FPGrowth, Charm and many other algorithms that can be used for association rule mining, frequent itemset mining, sequential pattern mining and sequential rule mining.