DMC Statistical Methods & Data Mining Concepts Flashcards

What does 'support' measure in association rule mining?

Answer: The frequency with which an itemset appears across the dataset

Support measures the frequency of an itemset appearing in the dataset, expressed as the proportion of all transactions that contain the itemset.

In the Apriori algorithm, the anti-monotone property states that:

Answer: Any superset of an infrequent itemset is also infrequent

The anti-monotone property states that any superset of an infrequent itemset cannot be frequent, enabling aggressive pruning of the candidate search space.

What does 'lift' measure in association rule mining?

Answer: How much more likely items are to co-occur compared to chance

Lift measures how much more frequently the antecedent and consequent appear together than would be expected if they were statistically independent.

Which data mining task involves predicting a continuous numerical value?

Answer: Regression

Regression predicts continuous numerical output values — such as predicting house prices, temperature, or revenue — based on input features.

What is 'feature engineering' in data mining?

Answer: Creating new input variables from existing data to improve model performance

Feature engineering involves transforming or combining existing features using domain knowledge to create new, more informative input variables that improve model accuracy.

What is the primary goal of 'dimensionality reduction' in data mining?

Answer: To reduce the number of input variables while preserving important information

Dimensionality reduction decreases input variable count while retaining as much useful information as possible, combating the curse of dimensionality.