Market Basket Analysis algorithm using Spark Mllib

[#1]

Hi experts,

I've the following dataset (just a example):

Customer_ID	Product_Desc
1	Jeans
1	T-Shirt
1	Food
2	Jeans
2	Food
2	Nightdress
2	T-Shirt
2	Hat
3	Jeans
3	Food
4	Food
4	Water
5	Water
5	Food
5	Beer

There exists any algorithm available that allows me to predictive Consumer Behavior like this: "When a customer buy a Jeans it also buys Food together"

The algorithms that I've found only calculate the most common products...not the association between them :( Anyone knows a good tutorial that shows me how can I predict the association that I said above?

The first step is to conclude this relationships:

Jeans-T-Shirt-Food

Jeans-Food-Nightdress-T-Shirt-Hat

Jeans-Food

Food-Water

Water-Food-Beer

Anyone have an Idea?

Many thanks!!!

[#2]

It sounds like you're looking for collaborative filtering, which does exist in spark.mllib: http://spark.apache.org/docs/1.6.2/mllib-collaborative-filtering.html

Amazon.com published a paper in 2003: "Amazon.com Recommendations: Item-to-Item Collaborative Filtering" which describes the algorithm in more detail. Quoting from the paper:

"the algorithm finds items similar to each of the user’s purchases and ratings, aggregates those items, and then recommends the most popular or correlated items".

[#3]

Alex Woolford many thanks for your help :) In your case how can plan this project as a machine learning project? What I'm seeing is that the algorithms that I've been seen only count the occurrences. Thanks!