Association Prediction by Rules Still Returns Itemsets

If I use this code with an association model, it still returns itemsets for me - when it should be returning only nodes with rules associated with them (according to sqlserverdatamining.com). If I try adding 'AND $PROBABILITY > .25' to the where clause, it returns 0 results for every query I try. Any clue why this may be happening?

Code Snippet

SELECT FLATTENED
(SELECT * FROM PredictAssociation([Product],20,
INCLUDE_NODE_ID,INCLUDE_STATISTICS)
WHERE $NODEID<>'')
FROM
[ProductRecommend]
PREDICTION JOIN
OPENQUERY([ds],
'SELECT
[PRODUCTCLASSID],[DESCRIPTION]
FROM
[Product_Table]
WHERE
[PRODUCTCLASSID] = ''1234'' AND [DESCRIPTION] = ''DESC''
') AS t

ON
[ProductRecommend].[Product].[PRODUCTCLASSID] = t.[PRODUCTCLASSID] AND
[ProductRecommend].[Product].[DESCRIPTION] = t.[DESCRIPTION]

This query returns more relevant results than those lacking the filtering by $NODEID, however the results should have higher probabilities than .047! Please help! Thanks!

Okay, I just reconstructed the same query using my data in a relational mining model (instead of OLAP) and got reasonable results. Would anyone know how to fix this for OLAP or be able to point me in a direction where I could go to learn how to do it? Thanks.

|||What is the MINIMUM_PROBABILITY value for the OLAP mining model?
Is it different than the one from the relational model?

One more thing (which you probably know already) -- the query you posted initially executes one prediction (PredictAssociation ... 20) for each row in the data source query (does not group together multiple input rows belonging to the same transaction).
Basically, only rules having a single item on the left hand side will be used in prediction.

Association model prediction not using itemsets

I have a market basket model using associations. It generated several dozen itemsets. However when I attempt to run a singleton prediction like this:

select (Predict(Orderproduct3q,INCLUDE_STATISTICS,10)) as [Recommendation]

From

[Case All]

NATURAL PREDICTION JOIN

(SELECT (SELECT '16407' AS [Pname])) AS t1

the resulting predictions don't take the itemsets into account. Instead, the predictions consist of the ranked products in the training set, ordered by frequency. This appears to happen regardless of the precise query specified within the "natural prediction join".

What's going on here and how do I generate a singleton prediction which makes use of the itemsets?

The nested table inside the input should have the same name as the nested table of the model, for Natural prediction Join. Could you please try this:

select (Predict(Orderproduct3q,INCLUDE_STATISTICS,10)) as [Recommendation]

From

[Case All]

NATURAL PREDICTION JOIN

(SELECT (SELECT '16407' AS [Pname]) AS Orderproduct3q ) AS t1

Otherwise, the input rowset (t1) contains an unnamed nested table which cannot be mapped to your model's table

|||

Thanks for the suggestion, which was on the right lines. Something like this does now pick up one of the itemsets.

select (Predict(Orderproduct3q,INCLUDE_STATISTICS,10)) as [Recommendation]

From

[Case All]

NATURAL PREDICTION JOIN
(SELECT (SELECT '17717' AS Pname Union SELECT '16415' AS Pname) as Orderproduct3q ) AS t1

|||Also look at the tip/trick at http://www.sqlserverdatamining.com/DMCommunity/TipsNTricks/3514.aspxsql

Association algorithm itemsets

What is the algorithm that generates the itemsets in the Association model? I'm looking to possibly use this part of the Association algorithm (i.e. the grouping into itemsets) in a separate plug-in algorithm.

The algorithm is based on the 'a priori' technique. Basically, 1-itemsets are filtered based on certain thresholds, then the algorithm moves to computing 2-itemsets and so on. Brief details on how Microsoft Association Rules work: http://msdn2.microsoft.com/en-us/library/ms174916.aspx

Hope this helps