Data Mining Chapter 6

A colorful and engaging illustration depicting data mining concepts like algorithms, itemsets, and analysis, with a backdrop of a computer and data charts.

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

25 Questions6 MinutesCreated by MiningExpert42

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

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

Data Mining Chapter 6

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