Finding association rules / frequent Itemsets - what are the application restrictions 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? 

 A: 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'.
A: If you want to be able to discover easily human-quantifiable "rules" in your data which predict which features are associated with high vs. low prices, then it sounds like what you want to do is run a decision tree-based statistical classifier on your data set, treating the "trip-price" field as the class variable.
A good example of a tree-based classifier is the C4.5 algorithm or its successor, C5.0.  The C5.0 algorithm is implemented in the R package C50, and a slightly modified version of C4.5 is also implemented in Weka as J48 (confusingly named, I know, but if you dig around a little bit you'll discover that it is indeed derived essentially from C4.5).  A short video course covering use of the GUI version of Weka can be found here, and in particular, a 10 minute video (section 3.4 in the course syllabus) which introduces J48 specifically can be found here.  There is also the CART (Classification And Regression Tree) algorithm as well; a version of that is implemented in the Python scikit-learn package.
Lastly, it's worth noting that tree-based classifiers are only needed if you actually want to have human-understandable predictive rules that you easily write down on a sheet of paper.  If you just want a computer to be able to accurately predict class outcomes (i.e., was it an expensive vacation or a cheap one) based on trip features (air conditioning, brand of hotel, country location, etc.) and you don't really care why the computer made a particular prediction, there are many other types of supervised machine learning algorithms that can help you as well.
A: One would be tempted to do this but the problem with this method is with categorization of the numeric field.
But mind you association rules can be overwhelming in number.
