McqMate
| 101. |
A fact is said to be partially additive if ___________. |
| A. | it is additive over every dimension of its dimensionality. |
| B. | additive over atleast one but not all of the dimensions. |
| C. | not additive over any dimension. |
| D. | none of the above. |
| Answer» B. additive over atleast one but not all of the dimensions. | |
| 102. |
A fact is said to be non-additive if ___________. |
| A. | it is additive over every dimension of its dimensionality. |
| B. | additive over atleast one but not all of the dimensions. |
| C. | not additive over any dimension. |
| D. | none of the above. |
| Answer» C. not additive over any dimension. | |
| 103. |
Non-additive measures can often combined with additive measures to create new _________. |
| A. | additive measures. |
| B. | non-additive measures. |
| C. | partially additive. |
| D. | all of the above. |
| Answer» A. additive measures. | |
| 104. |
A fact representing cumulative sales units over a day at a store for a product is a _________. |
| A. | additive fact. |
| B. | fully additive fact. |
| C. | partially additive fact. |
| D. | non-additive fact. |
| Answer» B. fully additive fact. | |
| 105. |
____________ of data means that the attributes within a given entity are fully dependent on the entire primary key of the entity. |
| A. | additivity. |
| B. | granularity. |
| C. | functional dependency. |
| D. | dependency. |
| Answer» C. functional dependency. | |
| 106. |
Which of the following is the other name of Data mining? |
| A. | exploratory data analysis. |
| B. | data driven discovery. |
| C. | deductive learning. |
| D. | all of the above. |
| Answer» D. all of the above. | |
| 107. |
Which of the following is a predictive model? |
| A. | clustering. |
| B. | regression. |
| C. | summarization. |
| D. | association rules. |
| Answer» B. regression. | |
| 108. |
Which of the following is a descriptive model? |
| A. | classification. |
| B. | regression. |
| C. | sequence discovery. |
| D. | association rules. |
| Answer» C. sequence discovery. | |
| 109. |
A ___________ model identifies patterns or relationships. |
| A. | descriptive. |
| B. | predictive. |
| C. | regression. |
| D. | time series analysis. |
| Answer» A. descriptive. | |
| 110. |
A predictive model makes use of ________. |
| A. | current data. |
| B. | historical data. |
| C. | both current and historical data. |
| D. | assumptions. |
| Answer» B. historical data. | |
| 111. |
____________ maps data into predefined groups. |
| A. | regression. |
| B. | time series analysis |
| C. | prediction. |
| D. | classification. |
| Answer» D. classification. | |
| 112. |
__________ is used to map a data item to a real valued prediction variable. |
| A. | regression. |
| B. | time series analysis. |
| C. | prediction. |
| D. | classification. |
| Answer» B. time series analysis. | |
| 113. |
In ____________, the value of an attribute is examined as it varies over time. |
| A. | regression. |
| B. | time series analysis. |
| C. | sequence discovery. |
| D. | prediction. |
| Answer» B. time series analysis. | |
| 114. |
In ________ the groups are not predefined. |
| A. | association rules. |
| B. | summarization. |
| C. | clustering. |
| D. | prediction. |
| Answer» C. clustering. | |
| 115. |
Link Analysis is otherwise called as ___________. |
| A. | affinity analysis. |
| B. | association rules. |
| C. | both a & b. |
| D. | prediction. |
| Answer» C. both a & b. | |
| 116. |
_________ is a the input to KDD. |
| A. | data. |
| B. | information. |
| C. | query. |
| D. | process. |
| Answer» A. data. | |
| 117. |
The output of KDD is __________. |
| A. | data. |
| B. | information. |
| C. | query. |
| D. | useful information. |
| Answer» D. useful information. | |
| 118. |
The KDD process consists of ________ steps. |
| A. | three. |
| B. | four. |
| C. | five. |
| D. | six. |
| Answer» C. five. | |
| 119. |
Treating incorrect or missing data is called as ___________. |
| A. | selection. |
| B. | preprocessing. |
| C. | transformation. |
| D. | interpretation. |
| Answer» B. preprocessing. | |
| 120. |
Converting data from different sources into a common format for processing is called as ________. |
| A. | selection. |
| B. | preprocessing. |
| C. | transformation. |
| D. | interpretation. |
| Answer» C. transformation. | |
| 121. |
Various visualization techniques are used in ___________ step of KDD. |
| A. | selection. |
| B. | transformaion. |
| C. | data mining. |
| D. | interpretation. |
| Answer» D. interpretation. | |
| 122. |
Extreme values that occur infrequently are called as _________. |
| A. | outliers. |
| B. | rare values. |
| C. | dimensionality reduction. |
| D. | all of the above. |
| Answer» A. outliers. | |
| 123. |
Box plot and scatter diagram techniques are _______. |
| A. | graphical. |
| B. | geometric. |
| C. | icon-based. |
| D. | pixel-based. |
| Answer» B. geometric. | |
| 124. |
__________ is used to proceed from very specific knowledge to more general information. |
| A. | induction. |
| B. | compression. |
| C. | approximation. |
| D. | substitution. |
| Answer» A. induction. | |
| 125. |
Describing some characteristics of a set of data by a general model is viewed as ____________ |
| A. | induction. |
| B. | compression. |
| C. | approximation. |
| D. | summarization. |
| Answer» B. compression. | |
| 126. |
_____________ helps to uncover hidden information about the data. |
| A. | induction. |
| B. | compression. |
| C. | approximation. |
| D. | summarization. |
| Answer» C. approximation. | |
| 127. |
_______ are needed to identify training data and desired results. |
| A. | programmers. |
| B. | designers. |
| C. | users. |
| D. | administrators. |
| Answer» C. users. | |
| 128. |
Overfitting occurs when a model _________. |
| A. | does fit in future states. |
| B. | does not fit in future states. |
| C. | does fit in current state. |
| D. | does not fit in current state. |
| Answer» B. does not fit in future states. | |
| 129. |
The problem of dimensionality curse involves ___________. |
| A. | the use of some attributes may interfere with the correct completion of a data mining task. |
| B. | the use of some attributes may simply increase the overall complexity. |
| C. | some may decrease the efficiency of the algorithm. |
| D. | all of the above. |
| Answer» D. all of the above. | |
| 130. |
Incorrect or invalid data is known as _________. |
| A. | changing data. |
| B. | noisy data. |
| C. | outliers. |
| D. | missing data. |
| Answer» B. noisy data. | |
| 131. |
ROI is an acronym of ________. |
| A. | return on investment. |
| B. | return on information. |
| C. | repetition of information. |
| D. | runtime of instruction |
| Answer» A. return on investment. | |
| 132. |
The ____________ of data could result in the disclosure of information that is deemed to be confidential. |
| A. | authorized use. |
| B. | unauthorized use. |
| C. | authenticated use. |
| D. | unauthenticated use. |
| Answer» B. unauthorized use. | |
| 133. |
___________ data are noisy and have many missing attribute values. |
| A. | preprocessed. |
| B. | cleaned. |
| C. | real-world. |
| D. | transformed. |
| Answer» C. real-world. | |
| 134. |
The rise of DBMS occurred in early ___________. |
| A. | 1950\s. |
| B. | 1960\s |
| C. | 1970\s |
| D. | 1980\s. |
| Answer» C. 1970\s | |
| 135. |
SQL stand for _________. |
| A. | standard query language. |
| B. | structured query language. |
| C. | standard quick list. |
| D. | structured query list. |
| Answer» B. structured query language. | |
| 136. |
Which of the following is not a data mining metric? |
| A. | space complexity. |
| B. | time complexity. |
| C. | roi. |
| D. | all of the above. |
| Answer» D. all of the above. | |
| 137. |
Reducing the number of attributes to solve the high dimensionality problem is called as ________. |
| A. | dimensionality curse. |
| B. | dimensionality reduction. |
| C. | cleaning. |
| D. | overfitting. |
| Answer» B. dimensionality reduction. | |
| 138. |
Data that are not of interest to the data mining task is called as ______. |
| A. | missing data. |
| B. | changing data. |
| C. | irrelevant data. |
| D. | noisy data. |
| Answer» C. irrelevant data. | |
| 139. |
______ are effective tools to attack the scalability problem. |
| A. | sampling. |
| B. | parallelization |
| C. | both a & b. |
| D. | none of the above. |
| Answer» C. both a & b. | |
| 140. |
Market-basket problem was formulated by __________. |
| A. | agrawal et al. |
| B. | steve et al. |
| C. | toda et al. |
| D. | simon et al. |
| Answer» A. agrawal et al. | |
| 141. |
Data mining helps in __________. |
| A. | inventory management. |
| B. | sales promotion strategies. |
| C. | marketing strategies. |
| D. | all of the above. |
| Answer» D. all of the above. | |
| 142. |
The proportion of transaction supporting X in T is called _________. |
| A. | confidence. |
| B. | support. |
| C. | support count. |
| D. | all of the above. |
| Answer» B. support. | |
| 143. |
The absolute number of transactions supporting X in T is called ___________. |
| A. | confidence. |
| B. | support. |
| C. | support count. |
| D. | none of the above. |
| Answer» C. support count. | |
| 144. |
The value that says that transactions in D that support X also support Y is called ______________. |
| A. | confidence. |
| B. | support. |
| C. | support count. |
| D. | none of the above. |
| Answer» A. confidence. | |
| 145. |
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 _______. |
| A. | 2% |
| B. | 20% |
| C. | 3% |
| D. | 30% |
| Answer» A. 2% | |
| 146. |
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 _______. |
| A. | 33.33% |
| B. | 66.66% |
| C. | 45% |
| D. | 50% |
| Answer» D. 50% | |
| 147. |
The left hand side of an association rule is called __________. |
| A. | consequent. |
| B. | onset. |
| C. | antecedent. |
| D. | precedent. |
| Answer» C. antecedent. | |
| 148. |
The right hand side of an association rule is called _____. |
| A. | consequent. |
| B. | onset. |
| C. | antecedent. |
| D. | precedent. |
| Answer» A. consequent. | |
| 149. |
Which of the following is not a desirable feature of any efficient algorithm? |
| A. | to reduce number of input operations. |
| B. | to reduce number of output operations. |
| C. | to be efficient in computing. |
| D. | to have maximal code length. |
| Answer» D. to have maximal code length. | |
| 150. |
All set of items whose support is greater than the user-specified minimum support are called as _____________. |
| A. | border set. |
| B. | frequent set. |
| C. | maximal frequent set. |
| D. | lattice. |
| Answer» B. frequent set. | |
| 151. |
If a set is a frequent set and no superset of this set is a frequent set, then it is called ________. |
| A. | maximal frequent set. |
| B. | border set. |
| C. | lattice. |
| D. | infrequent sets. |
| Answer» A. maximal frequent set. | |
| 152. |
Any subset of a frequent set is a frequent set. This is ___________. |
| A. | upward closure property. |
| B. | downward closure property. |
| C. | maximal frequent set. |
| D. | border set. |
| Answer» B. downward closure property. | |
| 153. |
Any superset of an infrequent set is an infrequent set. This is _______. |
| A. | maximal frequent set. |
| B. | border set. |
| C. | upward closure property. |
| D. | downward closure property. |
| Answer» C. upward closure property. | |
| 154. |
If an itemset is not a frequent set and no superset of this is a frequent set, then it is _______. |
| A. | maximal frequent set |
| B. | border set. |
| C. | upward closure property. |
| D. | downward closure property. |
| Answer» B. border set. | |
| 155. |
A priori algorithm is otherwise called as __________. |
| A. | width-wise algorithm. |
| B. | level-wise algorithm. |
| C. | pincer-search algorithm. |
| D. | fp growth algorithm. |
| Answer» B. level-wise algorithm. | |
| 156. |
The A Priori algorithm is a ___________. |
| A. | top-down search. |
| B. | breadth first search. |
| C. | depth first search. |
| D. | bottom-up search. |
| Answer» D. bottom-up search. | |
| 157. |
The first phase of A Priori algorithm is _______. |
| A. | candidate generation. |
| B. | itemset generation. |
| C. | pruning. |
| D. | partitioning. |
| Answer» A. candidate generation. | |
| 158. |
The second phaase of A Priori algorithm is ____________. |
| A. | candidate generation. |
| B. | itemset generation. |
| C. | pruning. |
| D. | partitioning. |
| Answer» C. pruning. | |
| 159. |
The _______ step eliminates the extensions of (k-1)-itemsets which are not found to be frequent, from being considered for counting support. |
| A. | candidate generation. |
| B. | pruning. |
| C. | partitioning. |
| D. | itemset eliminations. |
| Answer» B. pruning. | |
| 160. |
The a priori frequent itemset discovery algorithm moves _______ in the lattice. |
| A. | upward. |
| B. | downward. |
| C. | breadthwise. |
| D. | both upward and downward. |
| Answer» A. upward. | |
| 161. |
After the pruning of a priori algorithm, _______ will remain. |
| A. | only candidate set. |
| B. | no candidate set. |
| C. | only border set. |
| D. | no border set. |
| Answer» B. no candidate set. | |
| 162. |
The number of iterations in a priori ___________. |
| A. | increases with the size of the maximum frequent set. |
| B. | decreases with increase in size of the maximum frequent set. |
| C. | increases with the size of the data. |
| D. | decreases with the increase in size of the data. |
| Answer» A. increases with the size of the maximum frequent set. | |
| 163. |
MFCS is the acronym of _____. |
| A. | maximum frequency control set. |
| B. | minimal frequency control set. |
| C. | maximal frequent candidate set. |
| D. | minimal frequent candidate set. |
| Answer» C. maximal frequent candidate set. | |
| 164. |
Dynamuc Itemset Counting Algorithm was proposed by ____. |
| A. | bin et al. |
| B. | argawal et at. |
| C. | toda et al. |
| D. | simon et at. |
| Answer» A. bin et al. | |
| 165. |
Itemsets in the ______ category of structures have a counter and the stop number with them. |
| A. | dashed. |
| B. | circle. |
| C. | box. |
| D. | solid. |
| Answer» A. dashed. | |
| 166. |
The itemsets in the _______category structures are not subjected to any counting. |
| A. | dashes. |
| B. | box. |
| C. | solid. |
| D. | circle. |
| Answer» C. solid. | |
| 167. |
Certain itemsets in the dashed circle whose support count reach support value during an iteration move into the ______. |
| A. | dashed box. |
| B. | solid circle. |
| C. | solid box. |
| D. | none of the above. |
| Answer» A. dashed box. | |
| 168. |
Certain itemsets enter afresh into the system and get into the _______, which are essentially the supersets of the itemsets that move from the dashed circle to the dashed box. |
| A. | dashed box. |
| B. | solid circle. |
| C. | solid box. |
| D. | dashed circle. |
| Answer» D. dashed circle. | |
| 169. |
The itemsets that have completed on full pass move from dashed circle to ________. |
| A. | dashed box. |
| B. | solid circle. |
| C. | solid box. |
| D. | none of the above. |
| Answer» B. solid circle. | |
| 170. |
The FP-growth algorithm has ________ phases. |
| A. | one. |
| B. | two. |
| C. | three. |
| D. | four. |
| Answer» B. two. | |
| 171. |
A frequent pattern tree is a tree structure consisting of ________. |
| A. | an item-prefix-tree. |
| B. | a frequent-item-header table. |
| C. | a frequent-item-node. |
| D. | both a & b. |
| Answer» D. both a & b. | |
| 172. |
The non-root node of item-prefix-tree consists of ________ fields. |
| A. | two. |
| B. | three. |
| C. | four. |
| D. | five. |
| Answer» B. three. | |
| 173. |
The frequent-item-header-table consists of __________ fields. |
| A. | only one. |
| B. | two. |
| C. | three. |
| D. | four. |
| Answer» B. two. | |
| 174. |
The paths from root node to the nodes labelled 'a' are called __________. |
| A. | transformed prefix path. |
| B. | suffix subpath. |
| C. | transformed suffix path. |
| D. | prefix subpath. |
| Answer» D. prefix subpath. | |
| 175. |
The transformed prefix paths of a node 'a' form a truncated database of pattern which co-occur with a is called _______. |
| A. | suffix path. |
| B. | fp-tree. |
| C. | conditional pattern base. |
| D. | prefix path. |
| Answer» C. conditional pattern base. | |
| 176. |
The goal of _____ is to discover both the dense and sparse regions of a data set. |
| A. | association rule. |
| B. | classification. |
| C. | clustering. |
| D. | genetic algorithm. |
| Answer» C. clustering. | |
| 177. |
Which of the following is a clustering algorithm? |
| A. | a priori. |
| B. | clara. |
| C. | pincer-search. |
| D. | fp-growth. |
| Answer» B. clara. | |
| 178. |
_______ clustering technique start with as many clusters as there are records, with each cluster having only one record. |
| A. | agglomerative. |
| B. | divisive. |
| C. | partition. |
| D. | numeric. |
| Answer» A. agglomerative. | |
| 179. |
__________ clustering techniques starts with all records in one cluster and then try to split that cluster into small pieces. |
| A. | agglomerative. |
| B. | divisive. |
| C. | partition. |
| D. | numeric. |
| Answer» B. divisive. | |
| 180. |
Which of the following is a data set in the popular UCI machine-learning repository? |
| A. | clara. |
| B. | cactus. |
| C. | stirr. |
| D. | mushroom. |
| Answer» D. mushroom. | |
| 181. |
In ________ algorithm each cluster is represented by the center of gravity of the cluster. |
| A. | k-medoid. |
| B. | k-means. |
| C. | stirr. |
| D. | rock. |
| Answer» B. k-means. | |
| 182. |
In ___________ each cluster is represented by one of the objects of the cluster located near the center. |
| A. | k-medoid. |
| B. | k-means. |
| C. | stirr. |
| D. | rock. |
| Answer» A. k-medoid. | |
| 183. |
Pick out a k-medoid algoithm. |
| A. | dbscan. |
| B. | birch. |
| C. | pam. |
| D. | cure. |
| Answer» C. pam. | |
| 184. |
Pick out a hierarchical clustering algorithm. |
| A. | dbscan |
| B. | birch. |
| C. | pam. |
| D. | cure. |
| Answer» B. birch. | |
| 185. |
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. |
| Answer» C. clustering large applications based on randomized search. | |
| 186. |
BIRCH is a ________. |
| A. | agglomerative clustering algorithm. |
| B. | hierarchical algorithm. |
| C. | hierarchical-agglomerative algorithm. |
| D. | divisive. |
| Answer» C. hierarchical-agglomerative algorithm. | |
| 187. |
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. |
| Answer» A. cf tree. | |
| 188. |
The ________ algorithm is based on the observation that the frequent sets are normally very few in number compared to the set of all itemsets. |
| A. | a priori. |
| B. | clustering. |
| C. | association rule. |
| D. | partition. |
| Answer» D. partition. | |
| 189. |
The partition algorithm uses _______ scans of the databases to discover all frequent sets. |
| A. | two. |
| B. | four. |
| C. | six. |
| D. | eight. |
| Answer» A. two. | |
| 190. |
The basic idea of the apriori algorithm is to generate________ item sets of a particular size & scans the database. |
| A. | candidate. |
| B. | primary. |
| C. | secondary. |
| D. | superkey. |
| Answer» A. candidate. | |
| 191. |
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. |
| Answer» B. apriori-gen. | |
| 192. |
The basic partition algorithm reduces the number of database scans to ________ & divides it into partitions. |
| A. | one. |
| B. | two. |
| C. | three. |
| D. | four. |
| Answer» B. two. | |
| 193. |
___________and prediction may be viewed as types of classification. |
| A. | decision. |
| B. | verification. |
| C. | estimation. |
| D. | illustration. |
| Answer» C. estimation. | |
| 194. |
___________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. |
| Answer» B. prediction. | |
| 195. |
Prediction can be viewed as forecasting a_________value. |
| A. | non-continuous. |
| B. | constant. |
| C. | continuous. |
| D. | variable. |
| Answer» C. continuous. | |
| 196. |
_________data consists of sample input data as well as the classification assignment for the data. |
| A. | missing. |
| B. | measuring. |
| C. | non-training. |
| D. | training. |
| Answer» D. training. | |
| 197. |
Rule based classification algorithms generate ______ rule to perform the classification. |
| A. | if-then. |
| B. | while. |
| C. | do while. |
| D. | switch. |
| Answer» A. if-then. | |
| 198. |
____________ are a different paradigm for computing which draws its inspiration from neuroscience. |
| A. | computer networks. |
| B. | neural networks. |
| C. | mobile networks. |
| D. | artificial networks. |
| Answer» B. neural networks. | |
| 199. |
The human brain consists of a network of ___________. |
| A. | neurons. |
| B. | cells. |
| C. | tissue. |
| D. | muscles. |
| Answer» A. neurons. | |
| 200. |
Each neuron is made up of a number of nerve fibres called _____________. |
| A. | electrons. |
| B. | molecules. |
| C. | atoms. |
| D. | dendrites. |
| Answer» D. dendrites. | |
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