The relationship between a candidate and frequent itemsets lies in the process of association rule mining in data mining. Here's a step-by-step explanation:

1. Candidate itemsets: These are sets generated and tested for frequency in the dataset during the execution of an algorithm. They are potential frequent itemsets.

2. Frequent itemsets: These are the itemsets whose support is greater than or equal to a minimum support threshold. In other words, these are the itemsets that appear frequently in the dataset.

3. The relationship: A frequent itemset must be a candidate itemset, but the reverse is not always true. This is because all frequent itemsets are first generated as candidate itemsets, and then they are tested for frequency. If their frequency is above a certain threshold, they become frequent itemsets. So, option (a) "A frequent itemset must be a candidate itemset" is the correct answer.

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The relationship between a candidate and frequent itemsets lies in the process of association rule mining in data mining. Here's a step-by-step explanation:

1. Candidate itemsets: These are sets generated and tested for frequency in the dataset during the execution of an algorithm. They are potential frequent itemsets.

2. Frequent itemsets: These are the itemsets whose support is greater than or equal to a minimum support threshold. In other words, these are the itemsets that appear frequently in the dataset.

3. The relationship: A frequent itemset must be a candidate itemset, but the reverse is not always true. This is because all frequent itemsets are first generated as candidate itemsets, and then they are tested for frequency. If their frequency is above a certain threshold, they become frequent itemsets. So, option (a) "A frequent itemset must be a candidate itemset" is the correct answer.

Knowee AI · Accepted Answer

The relationship between a candidate and frequent itemsets lies in the process of association rule mining in data mining. Here's a step-by-step explanation:

1. Candidate itemsets: These are sets generated and tested for frequency in the dataset during the execution of an algorithm. They are potential frequent itemsets.

2. Frequent itemsets: These are the itemsets whose support is greater than or equal to a minimum support threshold. In other words, these are the itemsets that appear frequently in the dataset.

3. The relationship: A frequent itemset must be a candidate itemset, but the reverse is not always true. This is because all frequent itemsets are first generated as candidate itemsets, and then they are tested for frequency. If their frequency is above a certain threshold, they become frequent itemsets. So, option (a) "A frequent itemset must be a candidate itemset" is the correct answer.

What is the relation between a candidate and frequent itemsets?Question 26Answera.A frequent itemset must be a candidate itemsetb.Strong relationship with transactionsc.No relation between these twod.A candidate itemset is always a frequent itemset

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