FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -Association rule mining: (a) Itemset generation, (b) Rule generation. Apriori principle: All subsets of a frequent itemset must also be frequent. Apriori algorithm: Pruning to efficiently get all the frequent itemsets. Maximal frequent itemset: none of the immediate supersets are frequent.

Detailed explanation-2: -Association rule mining, at a basic level, involves the use of machine learning models to analyze data for patterns, or co-occurrences, in a database. It identifies frequent if-then associations, which themselves are the association rules. An association rule has two parts: an antecedent (if) and a consequent (then).

Detailed explanation-3: -1. An association rule having support and confidence greater than or equal to a user-specified minimum support threshold and respectively a minimum confidence threshold. Learn more in: Mining Association Rules.

Detailed explanation-4: -Frequent item sets, also known as association rules, are a fundamental concept in association rule mining, which is a technique used in data mining to discover relationships between items in a dataset.

Detailed explanation-5: -Frequent itemset mining naturally leads to the discovery of associations and correlations among items in large transaction data sets. The concept of association rule was introduced together with that of frequent itemset [2]. An association rule r takes the form of → , where and are itemsets, and ∩ = .