How to specify important attributes?
A possible solution is to calculate the information gain associated to each attribute:
$$I_{E}(f) = - \sum \limits_{i = 1}^m f_ilog_2f_i$$
Initially you have the whole dataset, and compute the information gain of each item. The item with the best information gain is the one you should use to partition the dataset (considering the item's values). Then, perform the same computations for each item (but the ones selected), and always choose the one which best describes/differentiates the entries from your dataset.
There are implementations available for such computations. Decision trees usually base their feature selection on the features with best information gain. You may use the resulting tree structure to find these important items.