I have a dataset that's purely categorical: for each item it's ranked across a set of attributes, whether it's easy, moderate or difficult.
But there are blanks if the item doesn't have the attribute. Currently I intend to encode blank as 0 if that's appropriate, and difficulty level as 1, 2, 3.
In this case, should I use Frequent Itemset Mining (Apriory) or k-modes if want to pinpoint which items are the most similar & dissimilar, and how could I identity what kind of attribute combinations result in such clustering? and how do I assess if the clustering result is good or need further tuning?