Classification of Datasets with Frequent Itemsets is Wild

The problem of dataset classification with frequent itemsets is defined as the problem of determining whether or not two different datasets have the same frequent itemsets without computing these itemsets explicitly. The reasoning behind this approach is high computational cost of computing frequent itemsets. Finding welldefined and understandable normal forms for this classification task would be a breakthrough in dataset classification field. The paper proves that classification of datasets with frequent itemsets is a hopeless task since canonical forms do not exist for this problem.