NOTE: TYPE ANSWERS ON COMPUTER DO NOT ATTACH PICTURE Q1: Describe the four gener

Question

NOTE: TYPE ANSWERS ON COMPUTER DO NOT ATTACH PICTURE

Q1:
Describe the four general optimization techniques for efficient computation of data cubes.

Q2:
Consider transaction table below to answer the following question.
TID   Items
T100   A, C, D, E
T101   A, C, E
T102   B, D, E, F
T103   A, D, E, F
T104   B, C, F
T105   A, B, C, D, E
If we set minimum support count equal to 50%, list all frequent itemsets along with their support count percentage.

Q3:
What is Association Rule? Discuss with example?

Q4:
What is Apriori algorithm, discuss its advantages and disadvantages?

Explanation / Answer

1) The four general optimizing techniques for the efficient computation of data cubes are as below

1.Smallet_parent

2.Cache results

3.Amortize scans

4.Share partitions

......................................................................................................................................................................

3) Association Rule: Finding the invisible information among the constituents in database.it is aimed to recognise the powerful rules found in database utilizing the few measures of engaging.depending on powerful rules Arun swamy and Rakesh agarwal together found the association rule to know the qualities among items in a huge transaction data entered with point of scale in case of supermarkets.

Example: {onion,Tomato } = {Tomata curry }

consider a database containing 3 transactions and 3 items in a super market.

Transaction id vegetables fruits salads

1. 1 0 1

2. 1 1 1

3. 0 0 0

Note: Here the '1' represents transaction takes place and '0' represents no transaction occur.

......................................................................................................................................................................4) Aprori algorithm: A repeated items Deduced with Aprori utilized to elucidate the association rules that emphasize the normal style in database,it utizes the botttom.up appearence.this algorithmends if no more sucessful instances are present.

Advantages:   1.it utilizes the huge item set property

2. simply parallelised.

3.simple to apply

Disadvantages: 1.supposes the transaction database was memory inhabitant.

2.Requires several database scans.

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