Support is defined on itemsets and gives the proportion of transactions which contain X . It is used as a measure of significance (importance) of an itemset. Since it basically uses the count of transactions it is often called a frequency constraint. An itemset with a support greater then a set minimum support threshold, supp(X)>σ , is called a frequent or large itemset. Supports main feature is that it possesses the down-ward closure property (anti-monotonicity) which means that all sub sets of a frequent set are also frequent. This property (actually, the fact that no super set of an infrequent set can be frequent) is used to prune the search space (usually thought of as a lattice or tree of item sets with increasing size) in level-wise algorithms (e.g., the Apriori algorithm). The disadvantage of support is the rare item problem. Items that occur very infrequently in the data set are pruned although they would still produce interesting and potentially valuable rules. The rare item problem is important for transaction data which usually have a very uneven distribution of support for the individual items (typical is a power-law distribution where few items are used all the time and most item are rarely used). … Support