Data Mining Final Exam Questions with
Correct Answers
Which of the following is a programming model of Bigdata? - Answer-Mapreduce
What operations are performed on Big data? - Answer-All of the above
Analytic
Semantic
Graph Processing
The characteristics of Big Data can be defined by at least 3-scale. In the following,
classify the phrases/statements according to the meaning of the 3-scale. - Answer-
Question 7 L10QQ
Which of the following is true (select all that apply)? - Answer-Deep learning is also
called Deep Neural Network
Deep learning processes unstructured or unlabeled data
Which of the following is not true? - Answer-Classification in deep learning is done
separately (from the feature extraction)
There are two main types of deep learning algorithms? What are they? - Answer-
Convolutional neural nets
Recurrent neural nets
TensorFlow is a kind of deep learning technique - Answer-Disagree
Beak detector in CNN can represent a small region with fewer parameters - Answer-
Right
Why do we need clustering? (Select all that apply) - Answer-Useful in data concept
construction
Pattern detection
Choose all correct statements (Select all that apply) - Answer-(Data) Objects
belonging to the same cluster are similar to each other.
(Data) Objects belonging to different clusters are dissimilar to each other.
Clustering deals with finding a structure in a collection of labeled data. - Answer-No
When we recursively add two or more appropriate clusters in clustering is called the
"divisive" type of clustering. - Answer-Agree
How many clusters are there in the following? - Answer-4
A clustering method starts with 1 point and recursively adds two or more appropriate
clusters. What are the methods here? - Answer-Agglomerative
, Which of the following is not used as the dissimilarity/similarity metric in clustering? -
Answer-Popularity
Clusters can be characterized by noise and outliers. - Answer-Right
Which of the following is a frequent pattern mining technique (select all that apply)? -
Answer-Association rule mining
Sequential pattern mining
In the case of the association rule mining, if sup = 20% & conf = 60%, describe what
do they mean with example? - Answer-20% of the customers buy bread and milk
together while each customer has a 60% of chance to buy milk if they bought bread
There are some challenges with the association rule mining. Sometimes it accepts
that all items in the dataset have similar frequencies. For example, if the frequencies
of items vary a great deal, we will encounter two problems. What are they? (select all
that apply) - Answer-Some rare items will not be found
Some frequent items will be associated with one another in all possible ways
Which of the following does not fall in the types of sequential pattern mining
algorithms? - Answer-Apriori
Suppose that you are given a set of sequences for sequential pattern mining. If you
need to find the complete set of frequent subsequences, it can be a form of -
Answer-Association rules
What do you mean by Support (A)? - Answer-Number of transactions containing A /
Total number of transactions
Which of the following is direct application of frequent itemset mining? - Answer-
Market Basket Analysis
Given the transactions in Table 1 and minsup s = 50% and k=3, how many frequent
k-itemsets are there? - Answer-0
Choose which data mining task is suitable for the following scenario: first, buy digital
camera, then buy large SD memory cards - Answer-Sequential pattern analysis
What does Apriori algorithm do? - Answer-It mines all frequent patterns through
pruning rules with lesser Support
Given the following database and rules. Consider min conf = 60% in database D.
Apply the Apriori algorithm to find accepted and rejected rules from the following
rules. - Answer-R1, R2, R5, R6 - ACCEPTEDR3, R4 - REJECTED
What is the relation between candidate and frequent itemsets? - Answer-A frequent
itemset must be a candidate itemset
Consider the following data transactions:
Correct Answers
Which of the following is a programming model of Bigdata? - Answer-Mapreduce
What operations are performed on Big data? - Answer-All of the above
Analytic
Semantic
Graph Processing
The characteristics of Big Data can be defined by at least 3-scale. In the following,
classify the phrases/statements according to the meaning of the 3-scale. - Answer-
Question 7 L10QQ
Which of the following is true (select all that apply)? - Answer-Deep learning is also
called Deep Neural Network
Deep learning processes unstructured or unlabeled data
Which of the following is not true? - Answer-Classification in deep learning is done
separately (from the feature extraction)
There are two main types of deep learning algorithms? What are they? - Answer-
Convolutional neural nets
Recurrent neural nets
TensorFlow is a kind of deep learning technique - Answer-Disagree
Beak detector in CNN can represent a small region with fewer parameters - Answer-
Right
Why do we need clustering? (Select all that apply) - Answer-Useful in data concept
construction
Pattern detection
Choose all correct statements (Select all that apply) - Answer-(Data) Objects
belonging to the same cluster are similar to each other.
(Data) Objects belonging to different clusters are dissimilar to each other.
Clustering deals with finding a structure in a collection of labeled data. - Answer-No
When we recursively add two or more appropriate clusters in clustering is called the
"divisive" type of clustering. - Answer-Agree
How many clusters are there in the following? - Answer-4
A clustering method starts with 1 point and recursively adds two or more appropriate
clusters. What are the methods here? - Answer-Agglomerative
, Which of the following is not used as the dissimilarity/similarity metric in clustering? -
Answer-Popularity
Clusters can be characterized by noise and outliers. - Answer-Right
Which of the following is a frequent pattern mining technique (select all that apply)? -
Answer-Association rule mining
Sequential pattern mining
In the case of the association rule mining, if sup = 20% & conf = 60%, describe what
do they mean with example? - Answer-20% of the customers buy bread and milk
together while each customer has a 60% of chance to buy milk if they bought bread
There are some challenges with the association rule mining. Sometimes it accepts
that all items in the dataset have similar frequencies. For example, if the frequencies
of items vary a great deal, we will encounter two problems. What are they? (select all
that apply) - Answer-Some rare items will not be found
Some frequent items will be associated with one another in all possible ways
Which of the following does not fall in the types of sequential pattern mining
algorithms? - Answer-Apriori
Suppose that you are given a set of sequences for sequential pattern mining. If you
need to find the complete set of frequent subsequences, it can be a form of -
Answer-Association rules
What do you mean by Support (A)? - Answer-Number of transactions containing A /
Total number of transactions
Which of the following is direct application of frequent itemset mining? - Answer-
Market Basket Analysis
Given the transactions in Table 1 and minsup s = 50% and k=3, how many frequent
k-itemsets are there? - Answer-0
Choose which data mining task is suitable for the following scenario: first, buy digital
camera, then buy large SD memory cards - Answer-Sequential pattern analysis
What does Apriori algorithm do? - Answer-It mines all frequent patterns through
pruning rules with lesser Support
Given the following database and rules. Consider min conf = 60% in database D.
Apply the Apriori algorithm to find accepted and rejected rules from the following
rules. - Answer-R1, R2, R5, R6 - ACCEPTEDR3, R4 - REJECTED
What is the relation between candidate and frequent itemsets? - Answer-A frequent
itemset must be a candidate itemset
Consider the following data transactions:
