Data Mining And Warehouse Set 6 MCQs

Q1 | _____________ helps to uncover hidden information about the data.

induction.

compression.

approximation.

summarization.

Q2 | _______ are needed to identify training data and desired results.

programmers.

designers.

users.

administrators.

Q3 | Overfitting occurs when a model _________.

does fit in future states.

does not fit in future states.

does fit in current state.

does not fit in current state.

Q4 | The problem of dimensionality curse involves ___________.

the use of some attributes may interfere with the correct completion of a data mining task.

the use of some attributes may simply increase the overall complexity.

some may decrease the efficiency of the algorithm.

all of the above.

Q5 | Incorrect or invalid data is known as _________.

changing data.

noisy data.

outliers.

missing data.

Q6 | ROI is an acronym of ________.

return on investment.

return on information.

repetition of information.

runtime of instruction

Q7 | The ____________ of data could result in the disclosure of information that is deemed to beconfidential.

authorized use.

unauthorized use.

authenticated use.

unauthenticated use.

Q8 | ___________ data are noisy and have many missing attribute values.

preprocessed.

cleaned.

real-world.

transformed.

Q9 | The rise of DBMS occurred in early ___________.

1950\s.

1960\s

1970\s

1980\s.

Q10 | SQL stand for _________.

standard query language.

structured query language.

standard quick list.

structured query list.

Q11 | Which of the following is not a data mining metric?

space complexity.

time complexity.

roi.

all of the above.

Q12 | Reducing the number of attributes to solve the high dimensionality problem is called as ________.

dimensionality curse.

dimensionality reduction.

cleaning.

overfitting.

Q13 | Data that are not of interest to the data mining task is called as ______.

missing data.

changing data.

irrelevant data.

noisy data.

Q14 | ______ are effective tools to attack the scalability problem.

sampling.

parallelization

both a & b.

none of the above.

Q15 | Market-basket problem was formulated by __________.

agrawal et al.

steve et al.

toda et al.

simon et al.

Q16 | Data mining helps in __________.

inventory management.

sales promotion strategies.

marketing strategies.

all of the above.

Q17 | The proportion of transaction supporting X in T is called _________.

confidence.

support.

support count.

all of the above.

Q18 | The absolute number of transactions supporting X in T is called ___________.

confidence.

support.

support count.

none of the above.

Q19 | The value that says that transactions in D that support X also support Y is called ______________.

confidence.

support.

support count.

none of the above.

Q20 | If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain jam,10000 transaction contain both bread and jam. Then the support of bread and jam is _______.

2%

20%

3%

30%

Q21 | 7 If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain jam,10000 transaction contain both bread and jam. Then the confidence of buying bread with jam is _______.

33.33%

66.66%

45%

50%

Q22 | The left hand side of an association rule is called __________.

consequent.

onset.

antecedent.

precedent.

Q23 | The right hand side of an association rule is called _____.

consequent.

onset.

antecedent.

precedent.

Q24 | Which of the following is not a desirable feature of any efficient algorithm?

to reduce number of input operations.

to reduce number of output operations.

to be efficient in computing.

to have maximal code length.

Q25 | All set of items whose support is greater than the user-specified minimum support are called as_____________.

border set.

frequent set.

maximal frequent set.

lattice.