Estimating destination according to previous data I need an advice. I can resume my problem like that :

I have some travels in a database, for example :

Person1 travelled from CityA to CityB on Date1
Person1 travelled from CityB to CityC on Date2
Person2 travelled from CityB to CityD on Date3
...


We can consider that these cities are in the complete graph.

Now, according to all the travels in the database, I would like to know where a PersonX is likely to go. I can know when he come from (or not).

I don't know if I should use machine learning, data-mining or graph theory.
This is a spatio-temporal clustering problem that is likely best solved with a Markov model. You could reasonable group this into machine learning or data mining. Develop your model using machine learning and then (the data mining part) leverage those pattern recognition techniques (that have been developed in machine learning). I think there are at least one or two threads on this over at Cross-Validated that go into more detail.  

Here are a couple of papers to look at if you are just getting started.

Using GPS to learn significant locations and predict movement across multiple users

Predicting Future Locations with Hidden Markov Models