Data mining

What is the primary advantage of the FP-Growth algorithm over the Apriori algorithm?

• FP-Growth requires more database scans than Apriori

• FP-Growth does not generate candidate itemsets

• FP-Growth is slower than Apriori

• FP-Growth does not use frequent patterns

What data structure is used in the FP-Growth algorithm to store frequent patterns efficiently?

• Hash Table

• Binary Search Tree

• FP-Tree (Frequent Pattern Tree)

• Linked List

Which of the following steps is NOT part of the FP-Growth algorithm?

• Constructing an FP-Tree

• Mining frequent patterns from the FP-Tree

• Generating candidate itemsets explicitly

• Creating conditional pattern bases

In the FP-Growth algorithm, what is the purpose of the header table?

• To store the support count of individual items

• To keep track of candidate itemsets

• To maintain a linked list of frequent items in descending order

• Both A and C

Which of the following is a limitation of the FP-Growth algorithm?

• It is inefficient for dense and long transactions

• It always requires multiple database scans

• It cannot be parallelized

• It cannot find frequent patterns

What is the primary function of the ID3 algorithm in data mining?

• Clustering data points

• Finding association rules

• Constructing decision trees for classification

• Reducing dimensionality of datasets

Which measure does the ID3 algorithm use to select the best attribute for splitting?

• Gini Index

• Information Gain

• Support and Confidence

• Mean Squared Error

What is the stopping condition for the ID3 algorithm when building a decision tree?

• When all attributes have been used

• When all instances belong to the same class

• When the dataset reaches a predefined size

• Both A and B

What is a major limitation of the ID3 algorithm?

• It cannot handle numerical attributes

• It is not suitable for classification problems

• It requires a predefined decision tree

• It does not use entropy in calculations

How does the ID3 algorithm handle missing values in the dataset?

• It removes the entire record

• It ignores the missing value and continues

• It replaces missing values with the most frequent attribute value

• ID3 does not handle missing values by itself