Data Mining And Warehouse Set 11

Computer Science Engineering (CSE),

Common Topics in Competitive and Entrance exams

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This set of Data Mining and Data Warehouse Multiple Choice Questions & Answers (MCQs) focuses on Data Mining And Warehouse Set 11

Q1 | Data redundancy between the environments results in less than ____________percent.
  • one.
  • two.
  • three.
  • four.
Answer: one.
Q2 | Bill Inmon has estimated___________of the time required to build a data warehouse, is consumedin the conversion process.
  • 10 percent.
  • 20 percent.
  • 40 percent
  • 80 percent.
Answer: 80 percent.
Q3 | The biggest drawback of the level indicator in the classic star-schema is that it limits_________
  • quantify.
  • qualify.
  • flexibility.
  • ability.
Answer: flexibility.
Q4 | Maintenance of cache consistency is the limitation of __________________.
  • numa.
  • unam.
  • mpp.
  • pmp.
Answer: mpp.
Q5 | ___________ of data means that the attributes within a given entity are fully dependent on theentire primary key of the entity.
  • additivity.
  • granularity.
  • functional dependency.
  • dimensionality.
Answer: functional dependency.
Q6 | Non-additive measures can often combined with additive measures to create new _________..
  • additive measures.
  • non-additive measures.
  • partially additive.
  • all of the above.
Answer: additive measures.
Q7 | ____________ of data means that the attributes within a given entity are fully dependent on theentire primary key of the entity.
  • additivity.
  • granularity.
  • functional dependency.
  • dependency.
Answer: functional dependency.
Q8 | _____________ helps to uncover hidden information about the data..
  • induction.
  • compression.
  • approximation.
  • summarization.
Answer: approximation.
Q9 | If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction containjam, 10000 transaction contain both bread and jam. Then the support of bread and jam is _______.
  • 2%
  • 20%
  • 3%
  • 30%
Answer: 2%
Q10 | 7 If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction containjam, 10000 transaction contain both bread and jam. Then the confidence of buying bread with jam is_______.
  • 33.33%
  • 66.66%
  • 45%
  • 50%
Answer: 50%
Q11 | The _______ step eliminates the extensions of (k-1)-itemsets which are not found to be frequent,from being considered for counting support.
  • candidate generation.
  • pruning.
  • partitioning.
  • itemset eliminations.
Answer: pruning.
Q12 | The transformed prefix paths of a node 'a' form a truncated database of pattern which co-occurwith a is called _______.
  • suffix path.
  • fp-tree.
  • conditional pattern base.
  • prefix path.
Answer: conditional pattern base.
Q13 | __________ clustering techniques starts with all records in one cluster and then try to split thatcluster into small pieces.
  • agglomerative.
  • divisive.
  • partition.
  • numeric.
Answer: divisive.
Q14 | BIRCH is a ________..
  • agglomerative clustering algorithm.
  • hierarchical algorithm.
  • hierarchical-agglomerative algorithm.
  • divisive.
Answer: hierarchical-agglomerative algorithm.
Q15 | The ________ algorithm is based on the observation that the frequent sets are normally very fewin number compared to the set of all itemsets.
  • a priori.
  • clustering.
  • association rule.
  • partition.
Answer: partition.
Q16 | The basic idea of the apriori algorithm is to generate________ item sets of a particular size &scans the database.
  • candidate.
  • primary.
  • secondary.
  • superkey.
Answer: candidate.
Q17 | ________is the most well known association rule algorithm and is used in most commercialproducts.
  • apriori algorithm.
  • partition algorithm.
  • distributed algorithm.
  • pincer-search algorithm.
Answer: apriori algorithm.
Q18 | An algorithm called________is used to generate the candidate item sets for each pass after thefirst.
  • apriori.
  • apriori-gen.
  • sampling.
  • partition.
Answer: apriori-gen.
Q19 | ___________can be thought of as classifying an attribute value into one of a set of possibleclasses.
  • estimation.
  • prediction.
  • identification.
  • clarification.
Answer: prediction.
Q20 | ____________ are a different paradigm for computing which draws its inspiration fromneuroscience.
  • computer networks.
  • neural networks.
  • mobile networks.
  • artificial networks.
Answer: neural networks.
Q21 | In a feed- forward networks, the conncetions between layers are ___________ from input tooutput.
  • bidirectional.
  • unidirectional.
  • multidirectional.
  • directional.
Answer: unidirectional.
Q22 | ___________ training may be used when a clear link between input data sets and target outputvalues does not exist.
  • competitive.
  • perception.
  • supervised.
  • unsupervised.
Answer: unsupervised.
Q23 | Investment analysis used in neural networks is to predict the movement of _________ fromprevious data.
  • engines.
  • stock.
  • patterns.
  • models.
Answer: stock.
Q24 | SOMs are used to cluster a specific _____________ dataset containing information about thepatient's drugs etc.
  • physical.
  • logical.
  • medical.
  • technical.
Answer: medical.
Q25 | _______ is concerned with discovering the model underlying the link structures of the web..
  • web content mining.
  • web structure mining.
  • web usage mining.
  • all of the above.
Answer: web structure mining.
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