FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -Partitioning: For this method, the number of database scans is significantly reduced. The basic idea here is that an itemset can only be frequent if it occurs in at least one of two database partitions. This means that the entire database only has to be scanned twice, which can be much more efficient.

Detailed explanation-2: -FP-growth uses a tree structure, called an FP-tree, to register all the frequent itemset information contained in the given data set, D. This requires only two scans of D. The frequent itemsets are then mined from the FP-tree.

Detailed explanation-3: -Apriori algorithm uses frequent itemsets to generate association rules. It is based on the concept that a subset of a frequent itemset must also be a frequent itemset. Frequent Itemset is an itemset whose support value is greater than a threshold value(support).

Detailed explanation-4: -Apriori algorithm is used for frequent itemset mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.

Detailed explanation-5: -Below are the steps for the apriori algorithm: Step-1: Determine the support of itemsets in the transactional database, and select the minimum support and confidence. Step-2: Take all supports in the transaction with higher support value than the minimum or selected support value.