Data Mining Chapter 6
Data Mining Knowledge Challenge
Test your understanding of Data Mining with our comprehensive quiz on algorithms and techniques from Chapter 6. This interactive quiz contains 25 multiple-choice questions that cover essential concepts, definitions, and practical applications in the field of data mining.
Participate to enhance your knowledge and see how well you grasp the intricacies of frequent itemset mining. Get ready to challenge yourself!
- 25 Thought-Provoking Questions
- Multiple Choice Format
- Immediate Feedback on Performance
A collection of one or more items is called as _____
Itemset
Support
Confidence
Support count
Frequency of occurrence of an itemset is called as _____
Support
Confidence
Support count
Rules
An itemset whose support is greater than or equal to a minimum support threshold is ______
Itemset
Frequent itemset
Infrequent items
Threshold values
What does FP growth algorithm do?
It mines all frequent patterns through pruning rules with lesser support.
It mines all frequent patterns through pruning rules with higher support.
It mines all frequent patterns by constructing an FP tree.
It mines all frequent patterns by constructing an itemsets.
What techniques can be used to improve the efficiency of apriori algorithm?
Hash-based techniques
Transaction Increases
Sampling
Cleaning
What do you mean by support(A)?
Total number of transactions containing A
Total Number of transactions not containing A
Number of transactions containing A / Total number of transactions
Number of transactions not containing A / Total number of transactions
How do you calculate Confidence (A -> B)?
Support(A ∩ B) / Support (A)
Support(A ∩ B) / Support (B)
Support(A ∪ B) / Support (A)
Support(A ∪ B) / Support (B)
Which of the following is the direct application of frequent itemset mining?
Social Network Analysis
Market Basket Analysis
Outlier Detection
Intrusion Detection
What is not true about FP growth algorithms?
It mines frequent itemsets without candidate generation
There are chances that FP trees may not fit in the memory
FP trees are very expensive to build
It expands the original database to build FP trees
When do you consider an association rule interesting?
If it only satisfies min_support
If it only satisfies min_confidence
If it satisfies both min_support and min_confidence
There are other measures to check so
What is the relation between a candidate and frequent itemsets?
A candidate itemset is always a frequent itemset
No relation between these two
Strong relation with transactions
A frequent itemset must be a candidate itemset
Which of the following is not a frequent pattern mining algorithm?
Apriori
FP growth
Decision trees
Eclat
Which algorithm requires fewer scans of data?
Apriori
FP Growth
Naive Bayes
Decision Trees
What will happen if support is reduced?
Number of frequent itemsets remains the same
Some itemsets will add to the current set of frequent itemsets
Some itemsets will become infrequent while others will become frequent
None of all above
What is association rule mining?
Same as frequent itemset mining
Using association to analyze correlation rules
Finding of strong association rules using frequent itemsets
Finding Itemsets for future trends
Apriori algorithm is one of the most commonly used algorithms for frequent pattern mining. It uses a “bottom-up” approach to identify frequent itemsets and then generates association rules from those itemsets.
True
False
In ECLAT algorithm, This algorithm uses a “depth-first search” approach to identify frequent itemsets. It is particularly efficient for datasets with a large number of items.
True
False
In ECLAT algorithm, this algorithm uses a “breadth-first search” approach to identify frequent itemsets. It is particularly efficient for datasets with a large number of items.
True
False
FP-growth algorithm, this algorithm uses a “compression” technique to find frequent patterns efficiently. It is particularly efficient for datasets with a large number of transactions
True
False
Frequent pattern mining has many applications, such as Social Network Analysis, Outlier Detection, Intrusion Detection
True
False
Subset function finds all the candidates contained in a transaction
True
False
Candidate itemsets are stored in a graph
True
False
Partition projection needs a lot of disk space, Parallel projection saves it
True
False
Deriving frequent patterns based on vertical intersections
True
False
Support(A) is equal Number of transactions not containing A / Total number of transactions
True
False
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