Maximal Frequent Item Set:
Solution
To find the maximal frequent item set, we need to follow these steps:
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Determine the support threshold: The support threshold is a minimum value that determines whether an item set is considered frequent or not. It is usually expressed as a percentage or a specific number of occurrences.
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Generate frequent item sets: Using a data mining algorithm like the Apriori algorithm, we can generate frequent item sets by scanning the dataset and identifying item sets that meet the support threshold.
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Remove subsets: From the generated frequent item sets, we need to remove any subsets that are also frequent. This is because a maximal frequent item set should not have any proper supersets that are also frequent.
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Identify maximal frequent item sets: After removing subsets, the remaining item sets are the maximal frequent item sets. These sets cannot be extended further without violating the support threshold.
By following these steps, we can identify the maximal frequent item sets in a given dataset.
Similar Questions
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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