3. [19] Frequent pattern and association m in in g

(a) [6] Since items have dierent 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 eciently in a transaction

database.

Answer: Suppose each item is associated with a group ID.

(b) [7] Suppose a BestBuy analyst is interested in only the

frequent patterns (i.e., itemsets) from the sales transactions that

satisfy certain constraints. For the following cases, state the

characteristics (i.e., categories) of every constraint in each

case and how to mine such patterns most eciently.

i. The prot range for the items in each pattern must be

within

$50.

Answer:

 

ii. The sum of the price of all the items with prot over $5 in

each pattern is at least $100.

Answer:

 

iii. The average prot for those items priced over $50 in each pattern

must be less than $10.

Answer:

 

(c) [6] Frequent p a t t e r n mining often generates m a n y somewhat

" similar" patterns that carry little new information. Give one such

example. Then outline one method that may generate l e s s number (i.e.,

compressed) b u t interesting patterns.

Answer:

ComputerScienceExpert

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Status NEW Posted 24 May 2017 02:05 AM My Price 8.00

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