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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A candidate itemset is always a frequent itemset
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A frequent itemset must be a candidate itemset
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No relation between these twos
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Strong relation with transactions
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Detailed explanation-1: -Candidate itemsets are generated using only the large itemsets of the previous pass without considering the transactions in the database. The large itemset of the previous pass is joined with itself to generate all itemsets whose size is higher by 1.
Detailed explanation-2: -An itemset consists of two or more items. An itemset that occurs frequently is called a frequent itemset. Thus frequent itemset mining is a data mining technique to identify the items that often occur together. For Example, Bread and butter, Laptop and Antivirus software, etc.
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: -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.