Top 350+ Solved Data Mining and Data Warehouse MCQ Questions Answer
Q. In ___________ each cluster is represented by one of the objects of the cluster located near thecenter.
a. k-medoid.
b. k-means.
c. stirr.
d. rock.
Q. CLARANS stands for _______.
a. clara net server.
b. clustering large application range network search.
c. clustering large applications based on randomized search.
d. clustering application randomized search.
Q. BIRCH is a ________.
a. agglomerative clustering algorithm.
b. hierarchical algorithm.
c. hierarchical-agglomerative algorithm.
d. divisive.
Q. The cluster features of different subclusters are maintained in a tree called ___________.
a. cf tree.
b. fp tree.
c. fp growth tree.
d. b tree.
Q. The ________ algorithm is based on the observation that the frequent sets are normally very few innumber compared to the set of all itemsets.
a. a priori.
b. clustering.
c. association rule.
d. partition.
Q. The partition algorithm uses _______ scans of the databases to discover all frequent sets.
a. two.
b. four.
c. six.
d. eight.
Q. The basic idea of the apriori algorithm is to generate________ item sets of a particular size & scansthe database.
a. candidate.
b. primary.
c. secondary.
d. superkey.
Q. An algorithm called________is used to generate the candidate item sets for each pass after the first.
a. apriori.
b. apriori-gen.
c. sampling.
d. partition.
Q. The basic partition algorithm reduces the number of database scans to ________ & divides it intopartitions.
a. one.
b. two.
c. three.
d. four.
Q. ___________and prediction may be viewed as types of classification.
a. decision.
b. verification.
c. estimation.
d. illustration.
Q. ___________can be thought of as classifying an attribute value into one of a set of possible classes.
a. estimation.
b. prediction.
c. identification.
d. clarification.
Q. Prediction can be viewed as forecasting a_________value.
a. non-continuous.
b. constant.
c. continuous.
d. variable.
Q. _________data consists of sample input data as well as the classification assignment for the data.
a. missing.
b. measuring.
c. non-training.
d. training.