What are the restrictions of application fields in searching for association rules (finding frequent itemsets)?
All examples I came across cover topic of 'true' basket-analysis in the sense of using a list of products which a sample of customers purchased with a goal to find rules such 'when one buys bread, it is likely butter is bought, too'.
What about more abstract applications? I mean finding any rules in dataset.
EXAMPLE. Let's assume I have a huge dataset with tourist-trip prices in 2013 year. The data includes trip-price and trip-features (such country of destination, days the travel lasts, accommodation condition elements, means of transport, extracurricular activities etc.). I want to find different associations between price and other trip features. My idea is to categorize price variable and find frequent itemsets among these trips (e.g. air conditioning=true, 5* hotel=true and Australia=true => high price=true).
- Is this a good way to work with such problems?
- Would you suggest any other general way of dealing with searching for any types of assocciations in different data sets?
Best Answer
I think what you might want to look at is 'Subgroup discovery', which is finding interesting rules with respect to a target variable .
http://sci2s.ugr.es/publications/ficheros/2011-Herrera-KAIS.pdf
Also see: Foundations of Rule Learning Authors: Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač ISBN: 978-3-540-75196-0 (Print) 978-3-540-75197-7 (Online) http://link.springer.com/book/10.1007%2F978-3-540-75197-7
Other areas to explore are 'contrast set mining' and 'emerging pattern mining' these and others sometimes go by the name of 'descriptive rule learning'.