Data Mining And Warehouse Set 6
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This set of Data Mining and Data Warehouse Multiple Choice Questions & Answers (MCQs) focuses on Data Mining And Warehouse Set 6
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.