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.
Why do association itemsets have probabilities associated with them when its rules that generate probabilities? Any queries I do against my model are using these itemset probabilities rather than the probabilities that the rules generate. More over, the probabilities generated for these itemsets are far less than the MINIMUM_PROBABILITY tag in the algorithm properties menu.
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]
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?
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?
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!
I need to create a set of cases for a project that uses the Microsoft Association Rules algorithm to make recommendations for products to customers. My question is: the set of scenarios must include all transactions of customers for training?. or is it sufficient some percentage of total transactions? If i do not use all transactions of customers, could be that the algorithm does not consider some products in their groups or rules and could not make recommendations about these?
MS uses the a priori algorithm in Association Rules, while other DM software have gone to the Novel Algorithm. Can you tell us why MS decided to stay with the a priori? Did you overcome the limitations that it's accused of having? Thanks!
In assotiation rules each rule has a [support, confidence] part. In Microsoft Association Rules there is a [probability,importance] measure in each rule and importance can be greater that 1.
I found the following in msdn but i'm not sure if i understood correctly.
MINIMUM_PROBABILITY: Specifies the minimum probability that a rule is true. For example, setting this value to 0.5 specifies that no rule with less than fifty percent probability is generated. The default is 0.4.
MAXIMUM_SUPPORT: Specifies the maximum number of cases in which an itemset can have support. If this value is less than 1, the value represents a percentage of the total cases. Values greater than 1 represent the absolute number of cases that can contain the itemset. The default is 1.
My questions are 1) Can i explain the [probability,importance] in [support,confidence]? If yes, how? 2) What importance>1 means?
i am trying to associate city in patient table --> disease in diseases table. I want to build association data mining model and use it on web form, such a way when the user enters city associated disease will be displayed.
should i select all 3 table to build the model? could help me to decide what tables should i select as Case and what tables as Nested? what attributes from the table should i select as key, input, predictive ?
i am using data mining tutorials on sqlserverdatamining.com to build this model. is there anything further during my model building i get into confusion? please suggest me where i can find complete resource or inform here.
i appreciate Mr.Jamie for his guidance so far in my academic project. i do have the book 'Data mining with sql server 2005'. I left with just one day to do this and document.
hoping someone could suggest. your help is much appreciated.
Hello, Let€™s say (for simplicity), in my site you can do one of two things €“ look at products and buy products. I want to build an association structure between my products based on those two actions, but(!) when a user looks at two products it creates less important association than when the user actually bought those two products. So basically, I want to give a different factor base on different actions occurred on my products. How do I build my structure? How do I query it?
I'm trying to figure out how to build a personalization engine.
If my structure is built with users as case, and products as nested - I€™d like to predict best products per user (rather than associated products), and If possible, ignoring products he already bought.
When i use the MS association rules ,i don't know how it is worked on the background .I stuy the Fp-Growth algorithm , but there're some questions , I don't kown what's the meaning of transcation database. who can give me one example ? thanks .I know we can store the data in relation database,but in basket Analysis ,how a transaction stroed in relation database?
Hi, I have a product basket scenario in where I have to recommend contracts to a customer based on the product and the quantity he/she buys. Product Quantity is an important factor which administers the user in the purchase of a particular contract I have the following tables with me. Customer product transaction table, Customer Contract transaction table but there is no direct relationship between contract and product in the database. The only way the two can be linked is through the customer. If I create a mining structure with Customer-Product information as the nested table and Customer-Contract information as the nested table with customer being the link between the two, the model is showing some irreverent contract recommendations. what is the solution for the above problem? Is it because the is no direct relationship between the product and the contract? How can I overcome this problem?
I'm wondering if anyone can give me some help with an association model I'd like to setup. It's a typical market-basket analysis, but rather than grouping by individual customers, I'd like to group by customer grouping. (In our database, customers are grouped into categories like: large, small, medium) If this is possible, I'd like to generate the most popular items (so just querying the most probable itemsets), for each customer grouping (I'll refer to this as 'segments' from here on out), and then create a listing of customers in each segment which do not have the most popular items for their segment. I know for this last part I can use reporting services to tackle that problem, however, I'm not really sure how I can really do the rest of this with an association model in SSAS.
Our table structure looks like this:
And the data is arranged in this fashion:
Code SnippetCustomer Table: CustomerName CustomerGroup ------------- ------------- A large B large C small
Purchases Table: CustomerName PurchasedProduct ------------ ---------------- A ProductA A ProductB B ProductA C ProductC C ProductD
I know this is a lot of information but any help you guys may be able to offer would be great! Thanks!
Hi there, it has been a long i'm trying to execute Microsoft Association Rules on my database.
I solved memory leak problem now, but i still can't understand output rules.
Database contain all the italian student who took a degree last year. Here in Italy, they have to compile a summary where they speak about universitary experience. ie: they talk about experience with teachers (pointage from 1 to 5); they says if they want to continue in the universitary field or not, and so on.
Most of the rules, says: Int_Stud=1-2, RapDoc>4
Int_Stud is the column where i store student intention to continue university. 1 means they want to go on, 2 means they do not want to continue to study. So, this rules has no sense, because it relates all the student (in my mind): the one who wants to continue university and the one who do not want to.
I think problem is that visual studio 2005 and analysis service has no understanding of Int_Stud world, they've no idea that Int_Stud can have just 2 values and that they're opposite each other. Is there a solution to this problem? Can i discretize this column?
Even if I know not to have perfect english, I hope to be understandable
I note that there exist three web viewers for data mining algorithms, namely, DMNaiveBayesViewer, DMDecisionTreeViewer and DMClusterViewer. How come there are no viewers for association rules (itemsets, rules, dependency network)? Can you suggest any alternative way of showing such valuable information in a web application?
I understand Mr. MacLennan's explanation provided at http://forums.microsoft.com/MSDN/ShowPost.aspx?PostID=282651&SiteID=1 and appreciate the time he took to explain how importance works. However, like the user with username "sang", I also ran the data in BI 2005 and got the same results listed by the aforementioned user. I did this using the following data:
The rule muffin -> donut has an importance of -0.105302438, which is not the same as Mr. MacLennan's results. I tried switching the roles of a and b in a -> b and using different bases on the logarithms. I don't get the result of -0.105302438 with any of these. I also tried to calculate importance with a small data set I have and can't get the results using Mr. MacLennan's explanation with that data set either. Any thoughts on the descrepancy?
I am doing the Market basket analysis for a retailer using association rule. The whole data set is huge which contains grocery, clothes and books etc. If I want to check out the relationship between several different clothes brands, (e.g. LEVI'S and adidas), should I just remove all the grocery and books transactions, use the subset which only contains clothes transactions to re-run the association rules? Is this gonna work?
hi, i have a exercise using association datamining my database have 350 records, i use 90 records for datamining and it release some rules which i choose on top of mSOLAP_NODE_SCORE, but when i use select statement to check my result i have 1 records, the same as my result, and 5 records not true; for example: rules A=a,B=b-> C=c select * from <my_table> where A='a' and B='b' and C='c'; ==>1 record return select * from <my_table> where A='a' and B='b' and C<>'c'; ==>5 records return C with 3 values c1,c2,c with the second statement C includes 2 c1 and 3 c2
i don't understand how they work. i want to choose some best rules can present my database. how can i choose importance and probability to get best rules. with database have 90 records and a database have 350 records which values i should use for minimum_probability, Minimum_Support, Minimum_importance... when i choose rules i should choose on importance or probability.
I want to score my data by only the assoziation rules I filtered in Mining-Model-Viewer.
Is this possible?
I recognized that MiningModel Predictionquery uses the generated Model (all rules).
Is there any way to influence the model at generating time.
Thanks a lot.
I specified the point to solve my problem but I did not understand why MS SQL Server didn't recognize the Association parameters I but in. It uses the Minimum_Probability I put in, but the Minimum_Importance i wrote didn't care it, it use the default value.
I want to paste a screenshot here but it isn't possible. By the way I'm using the evaluation version, which should be same as the enterprise edition.
I'm new to analysis services and hopefully this is a quick & easy question. I have a couple of quite large (163,000 tuple) tables with columns essentially representing a bit vector. I would like to mine for association rules but the number of '1' values are very, very sparse and they are the only objects of interest. How can I get more control over the algorithm---that is, how can I stipulate that the state of the column must be '1' to be considered? Any help or direction to the proper documentation would be great.
I'm building a mining model wiht MS Association Rules. After processing this model, the result includes some rules(example):
E = Existing, C = Existing -> B = Existing F = Existing -> E = Existing C = Existing, B = Existing -> E = Existing F = Existing -> B = Existing B = Existing, A = Existing -> C = Existing F = Existing, B = Existing -> E = Existing F = Existing, E = Existing -> B = Existing D = Existing -> A = Existing C = Existing -> A = Existing E = Existing, A = Existing -> B = Existing
I want to buid a query that has two or more items on the left of the rules, example: E = Existing, C = Existing -> B = Existing ->I want to buid a query to predict that: when a customer buy 'E' and 'C' then he likely buys 'B'
I read the paper of sequence clustering. It seems that the major application of the algorithm is for the web site. I was just thinking that can I apply this algorithm on the purchase sequences of credit card data?
If so,please also tell me the difference between sequence clustering and association rules on credit card data application. Although I realize that sequence clustering is a fully probabilistic model and it has the capability of prediction, association rules also give the probabilities of purchasing the other products.