Emerging patterns (EPs) are itemsets having supports that change significantly from one dataset to another. A classifier, CAEP, is disclosed using the following main ideas based on EPs: (i) Each EP can sharply differentiate the class membership of a (possibly small) fraction of instances containing the EP, due to the big difference between the EP's supports in the opposing classes; the differentiating power of the EP is defined in terms of the EP's supports and ratio, on instances containing the EP. (ii) For each instance t, by aggregating (124) the differentiating power of a fixed, automatically selected set of EPs, a score is obtained for each class (126). The scores for all classes are normalized (144) and the largest score determines t's class (146). CAEP is suitable for many applications, even those with large volumes of high dimensional data. CAEP does not depend on dimension reduction on data and is usually equally accurate on all classes even if their populations are unbalanced.