Dear All,
I have already posted before on the list about data mining and it has
proved very useful.
I have now a training dataset consisting of N objects of M<<N different
kinds (actually, M is usually 3 to 5, whereas N is of the order of 1000).
Every object has its own label L_i, i=1...N, that is known.
For each of these objects I measure some property in time (let's say I
measure it Q times in a given time interval), i.e. the i-th object has
an associated file {t, y}, where t=(t_1,t_2....t_Q) and y=(y_1,y_2,...y_Q).
My problem is then to come up with an algorithm that after learning on
the training dataset, can guess the labels of a testing dataset.
The difference with respect to the datamining I have done so far is that
I do not have a set of properties for every object (e.g. age, sex,
income, etc...) but rather an associated function y=f(t).
Any suggestion (either conceptual or about which R package I should turn
to) is greatly appreciated.
Many thanks
Lorenzo
