Thus, maximal itemsets are unsuitable for association rule mining from frequent itemsets.
Contrasting, the use of maximal itemsets leads to biclusters with the columns' size maximized.
For this category, several algorithms have been proposed, among them, we can mention close algorithm, Pascal, etc. Mining maximal itemsets An itemset is maximal frequent if none of its immediate supersets is frequent.
It stores only maximal itemsets, and can be used to derive the information of the subset itemsets in a maximal itemset through a hash function.
Having low cardinality, maximal itemsets can be used to reproduce all the frequent itemsets.
In addition, the generation of frequent itemsets from maximal itemsets may result in an intractable number of duplicates.
The GenMax algorithm described in [19] mines maximal itemsets using a tidset intersection approach.
It is a lossy compression in the sense that all subsets of maximal itemsets are also frequent, but the support value of each subset itemset is not known.
For example, the redundancy in itemsets can be decreased by ignoring irrelevant rules [ 34] or by limiting the search space to certain itemset classes, such as maximal itemsets [ 35, 36] or the highest scoring itemsets (top-K) [ 37].
For this purpose, expression values are discretized, and association rules are generated from maximal itemsets [ 72].
