Since items have different expected frequencies of sales, it is desirable to use

Question

Since items have different expected frequencies of sales, it is desirable to use group-based minimum support thresholds set up by users. For example, one may set up a small min support for the group of cameras but a rather large one for the group of bread. Outline an FP growth like algorithm that derive the set of frequent items efficiently in a transaction database. Suppose each item is associated with a group ID Suppose a Best Buy analyst is interested in only the frequent patterns (ie., item sets) from the soles transact, that satisfy certain constraints. For the

Explanation / Answer

As per the conditions given in the problem apriori algorithm will be the best suite for this condition. It is used for frequent item set mining. It basically proceeds by identifying the frequent individual items in the database and then extends the same to much larger and larger datasets.

Apriori algorithm should work in this case.

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