Mining Association Rules Quiz

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Association rules basics

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20524UCSC022 MALAVIGA M

India

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Mining Association Rules Quiz


Mining Association Rules QuizVersión en línea Association rules basics por 20524UCSC022 MALAVIGA M 1 In a single-dimensional Boolean association rule, what does the rule express in a transaction? a If X occurs in a transaction, then Y is likely to occur b If X occurs, Y must occur in every case c The rule only uses numerical values, not items d X and Y are independent of each other 2 What is the support of an itemset? a Probability of the rule being true b Proportion of transactions containing the itemset c Number of transactions in the database d Count of items in the itemset 3 What does confidence measure in association rules? a Probability of Y given X b Lift between X and Y c Probability of X given Y d Support of X 4 What is lift in association rules? a Difference between X and Y supports b Probability of both X and Y independently c Ratio of observed support to expected support if X and Y were independent d Sum of supports of X and Y 5 What are multilevel association rules? a Rules using only numerical attributes b Rules derived without any hierarchy c Rules across time periods d Rules that involve item hierarchies across levels 6 What are multidimensional association rules? a Rules that ignore user-defined dimensions b Rules within a single table c Rules across multiple dimensions in a data warehouse d Rules only for time-series data 7 Where are multilevel rules most naturally mined? a Hierarchical item databases or data warehouses b Isolated spreadsheets with no hierarchy c Flat text files only d Streaming sensor data only 8 What is a primary difference between transaction databases and data warehouses for rule mining? a Transaction DBs store only numerical data b Transactions are flat and short-term; warehouses are integrated, historical c Warehouses are smaller than transaction DBs d Warehouses do not support queries or analysis 9 Which metric helps prune weak rules in many mining tasks? a High lift with high confidence b Low support with high confidence c Low confidence and lift near 1 d All of the above e High support with low confidence 10 What is the primary purpose of decision tree induction in classification? a Maximize entropy without splits b Build a tree model that classifies data by feature-based splits c Store data in leaves only d Compute regression coefficients 11 Which algorithm is commonly used for Bayesian classification? a K-means clustering b Decision tree induction c Support vector machine d Naive Bayes classifier 12 In backpropagation, what is updated during learning? a Output labels only b Input data samples c Weights of the neural network connections d Activation functions 13 Classification concepts from Association Rule Mining derive rules relating what? a Antecedents to consequents (if-then rules) b Decision boundaries c Feature importances d Cluster centroids 14 What does classifier accuracy measure? a Proportion of correct predictions b Model training time c Number of features d Prediction latency 15 Name a common issue in classification that can harm prediction quality. a Zero training error always b Infinite model complexity c Overfitting to training data d Underfitting only 16 Which criterion is often used to build a decision tree? a Correlation coefficient b Cosine similarity c Information gain or Gini impurity d Euclidean distance 17 What is a key difference between Naive Bayes and decision trees? a Naive Bayes is slower b Naive Bayes assumes feature independence; trees do not c Trees require labeled data only d Naive Bayes uses deep learning 18 What does backpropagation optimize in neural nets? a The error between predicted and true outputs b Data normalization c Rule-based decisions d Input dimensionality 19 How is prediction accuracy commonly computed? a Correct predictions divided by total predictions b Number of parameters updated per epoch c Sum of all probabilities d Predicted minus actual labels Feedback 1 The rule links presence of X with Y; it is not guaranteed (probabilistic). 2 Support is a frequency proportion, not a raw count or probability of the rule itself. 3 Confidence = P(Y\|X), not P(X\|Y) or support, and not a rank measure. 4 Lift >1 indicates positive dependence; it compares observed vs. expected under independence. 5 They exploit hierarchies like product category, subcategory, item level. 6 They combine dimensions like customer, product, store in warehouses. 7 Hierarchies exist in catalogs, product trees, or feature families. 8 Warehouses support historical, consolidated analysis; transactions are day-to-day. 9 Weak rules have low confidence or lift close to 1; pruning uses these cues. 10 Decision trees partition data using feature values to assign class labels. 11 Naive Bayes applies Bayes’ rule with often strong feature independence assumptions. 12 Backpropagation adjusts edge weights to minimize error. 13 Association rules relate antecedent itemsets to consequent outcomes or classes. 14 Accuracy = correct predictions divided by total predictions. 15 Overfitting captures noise instead of signal, reducing generalization. 16 Splits are chosen to maximize information gain or minimize impurity. 17 Naive Bayes uses probabilistic independence assumptions; trees partition data hierarchically. 18 Backpropagation propagates error gradients to update weights to reduce error. 19 Accuracy = (true positives + true negatives) / total cases.

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Mining Association Rules Quiz

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Mining Association Rules QuizVersión en línea

por 20524UCSC022 MALAVIGA M

In a single-dimensional Boolean association rule, what does the rule express in a transaction?

What is the support of an itemset?

What does confidence measure in association rules?

What is lift in association rules?

What are multilevel association rules?

What are multidimensional association rules?

Where are multilevel rules most naturally mined?

What is a primary difference between transaction databases and data warehouses for rule mining?

Which metric helps prune weak rules in many mining tasks?

What is the primary purpose of decision tree induction in classification?

Which algorithm is commonly used for Bayesian classification?

In backpropagation, what is updated during learning?

Classification concepts from Association Rule Mining derive rules relating what?

What does classifier accuracy measure?

Name a common issue in classification that can harm prediction quality.

Which criterion is often used to build a decision tree?

What is a key difference between Naive Bayes and decision trees?

What does backpropagation optimize in neural nets?

How is prediction accuracy commonly computed?

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