McqMate
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) , Information Technology Engineering (IT) .
| 151. |
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 | |
| 152. |
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 | |
| 153. |
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 | |
| 154. |
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 | |
| 155. |
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 | |
| 156. |
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 | |
| 157. |
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 | |
| 158. |
The first phase of A Priori algorithm is___________ |
| A. | Candidate generation |
| B. | Itemset generation |
| C. | Pruning |
| D. | Partitioning |
| Answer» A. Candidate generation | |
| 159. |
The second phase of A Priori algorithm is____________ |
| A. | Candidate generation |
| B. | Itemset generation |
| C. | Pruning |
| D. | Partitioning |
| Answer» C. Pruning | |
| 160. |
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 | |
| 161. |
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 | |
| 162. |
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 | |
| 163. |
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 | |
| 164. |
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 | |
| 165. |
Dynamic 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 | |
| 166. |
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 | |
| 167. |
The itemsets in the_________category structures are not subjected to any counting |
| A. | Dashes |
| B. | Box |
| C. | Soli |
| D. | D Circle |
| Answer» C. Soli | |
| 168. |
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 | |
| 169. |
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 | |
| 170. |
The item sets 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 | |
| 171. |
The FP-growth algorithm has phases |
| A. | One |
| B. | Two |
| C. | Three |
| D. | Four |
| Answer» B. Two | |
| 172. |
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 | |
| 173. |
The non-root node of item-prefix-tree consists of fields |
| A. | Two |
| B. | Three |
| C. | Four |
| D. | Five |
| Answer» B. Three | |
| 174. |
The frequent-item-header-table consists of fields |
| A. | Only one. |
| B. | Two. |
| C. | Three. |
| D. | Four |
| Answer» B. Two. | |
| 175. |
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 | |
| 176. |
The transformed prefix paths of a node 'a' form a truncated database of pattern which cooccur with a is called________ |
| A. | Suffix path |
| B. | FP-tree |
| C. | Conditional pattern base |
| D. | Prefix path |
| Answer» C. Conditional pattern base | |
| 177. |
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 | |
| 178. |
Which of the following is a clustering algorithm? |
| A. | A priori |
| B. | CLARA |
| C. | Pincer-Search |
| D. | FP-growth |
| Answer» B. CLARA | |
| 179. |
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 | |
| 180. |
clustering techniques starts with all records in one cluster and then try to split that |
| A. | Agglomerative. |
| B. | Divisive. |
| C. | Partition. |
| D. | Numeric |
| Answer» B. Divisive. | |
| 181. |
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 | |
| 182. |
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 | |
| 183. |
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 | |
| 184. |
Pick out a k-medoid algorithm |
| A. | DBSCAN |
| B. | BIRCH |
| C. | PAM |
| D. | CURE |
| Answer» C. PAM | |
| 185. |
Pick out a hierarchical clustering algorithm |
| A. | DBSCAN |
| B. | CURE |
| C. | PAM |
| D. | BIRCH |
| Answer» D. BIRCH | |
| 186. |
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 | |
| 187. |
BIRCH is a________ |
| A. | Agglomerative clustering algorithm |
| B. | Hierarchical algorithm |
| C. | Hierarchical-agglomerative algorithm |
| D. | Divisive |
| Answer» C. Hierarchical-agglomerative algorithm | |
| 188. |
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 | |
| 189. |
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 | |
| 190. |
The partition algorithm uses scans of the databases to discover all frequent sets |
| A. | Two |
| B. | Four |
| C. | Six |
| D. | Eight |
| Answer» A. Two | |
| 191. |
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 | |
| 192. |
is the most well-known association rule algorithm and is used in most commercial products |
| A. | Apriori algorithm |
| B. | Partition algorithm |
| C. | Distributed algorithm |
| D. | Pincer-search algorithm |
| Answer» A. Apriori algorithm | |
| 193. |
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 | |
| 194. |
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 | |
| 195. |
and prediction may be viewed as types of classification |
| A. | Decision. |
| B. | Verification. |
| C. | Estimation. |
| D. | Illustration |
| Answer» C. Estimation. | |
| 196. |
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. | |
| 197. |
Prediction can be viewed as forecasting a value |
| A. | Non-continuous. |
| B. | Constant. |
| C. | Continuous. |
| D. | variable |
| Answer» C. Continuous. | |
| 198. |
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» B. Measuring. | |
| 199. |
Rule based classification algorithms generate_________rule to perform the classification |
| A. | If-then |
| B. | While |
| C. | Do while |
| D. | Switch |
| Answer» A. If-then | |
| 200. |
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 | |
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