DATABASE FUNDAMENTALS
DATA WAREHOUSING AND DATA MINING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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It mines all frequent patterns through pruning rules with lesser support
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It mines all frequent patterns through pruning rules with higher support
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Both a and b
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None of the above
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Detailed explanation-1: -The Apriori algorithm is used for mining frequent itemsets and devising association rules from a transactional database. The parameters “support” and “confidence” are used. Support refers to items’ frequency of occurrence; confidence is a conditional probability. Items in a transaction form an item set.
Detailed explanation-2: -Apriori is an algorithm for frequent item set 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-3: -Apriori algorithm was the first algorithm that was proposed for frequent itemset mining. It was later improved by R Agarwal and R Srikant and came to be known as Apriori. This algorithm uses two steps “join” and “prune” to reduce the search space. It is an iterative approach to discover the most frequent itemsets.
Detailed explanation-4: -Apriori algorithm is a sequence of steps to be followed to find the most frequent itemset in the given database. This data mining technique follows the join and the prune steps iteratively until the most frequent itemset is achieved. A minimum support threshold is given in the problem or it is assumed by the user.
Detailed explanation-5: -The Apriori algorithm is a seminal algorithm for mining frequent itemsets for Boolean association rules. It explores the level-wise mining Apriori property that all nonempty subsets of a frequent itemset must also be frequent.