FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -Apriori algorithm: This is one of the most commonly used algorithms for frequent pattern mining. It uses a “bottom-up” approach to identify frequent itemsets and then generates association rules from those itemsets.

Detailed explanation-2: -EClaT algorithm Equivalence Class Transformation (EClaT) (Zaki 2000) is an algorithm that mines frequent itemsets efficiently using the vertical data format as shown in Table 3. In this method of data representation, all the transactions that contain a particular itemset are grouped into the same record.

Detailed explanation-3: -Definition. Frequent Pattern Mining is a Data Mining subject with the objective of extracting frequent itemsets from a database. Frequent itemsets play an essential role in many Data Mining tasks and are related to interesting patterns in data, such as Association Rules.

Detailed explanation-4: -Frequent patterns are itemsets, subsequences, or substructures that appear in a data set with frequency no less than a user-specified threshold. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set, is a frequent itemset.