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Unit 3: Mining frequent patterns - Practice Quiz

BTY587 — Data Analysis And Simulations 60 Questions
0 Correct 0 Wrong 60 Left
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1 In data mining, an association between items refers to:

associations and correlations Easy
A. A relationship where items tend to occur together in transactions
B. A method to sort items alphabetically
C. A technique to compress large datasets
D. A way to encrypt transaction records

2 A correlation measures:

associations and correlations Easy
A. The physical storage size of a dataset
B. The speed of a mining algorithm
C. The total number of transactions in a database
D. The statistical relationship or dependency between two variables

3 Which of the following best describes the difference between association and correlation?

associations and correlations Easy
A. Correlation is used only for encryption
B. They are exactly the same concept
C. Association applies only to numbers, correlation only to text
D. Association shows co-occurrence, while correlation measures strength and direction of dependency

4 An association rule is typically written in the form:

Introduction to Association Rule Mining Easy
A.
B.
C.
D.

5 The support of an itemset is defined as:

Introduction to Association Rule Mining Easy
A. The order in which items are scanned
B. The number of columns in the dataset
C. The fraction of transactions that contain the itemset
D. The average price of items

6 The confidence of the rule is calculated as:

Introduction to Association Rule Mining Easy
A.
B.
C.
D.

7 Which classic algorithm is widely used to mine frequent itemsets for association rules?

Introduction to Association Rule Mining Easy
A. Apriori
B. QuickSort
C. K-Means
D. Dijkstra

8 A frequent itemset is one whose support is:

Introduction to Association Rule Mining Easy
A. Less than the average transaction size
B. Always equal to zero
C. Equal to the number of attributes
D. Greater than or equal to a minimum support threshold

9 Which measure is commonly used to evaluate correlation between items in association analysis?

Mining Correlation Patterns Easy
A. Radius
B. Lift
C. Latency
D. Depth

10 A lift value equal to indicates that items and are:

Mining Correlation Patterns Easy
A. Impossible to occur together
B. Perfectly positively correlated
C. Perfectly negatively correlated
D. Independent of each other

11 A lift value greater than suggests that two items are:

Mining Correlation Patterns Easy
A. Completely unrelated
B. Positively correlated
C. Negatively correlated
D. Never purchased together

12 The main reason for mining correlation patterns rather than only association rules is to:

Mining Correlation Patterns Easy
A. Reduce the total number of transactions stored
B. Identify whether an association is statistically meaningful and not just frequent
C. Encrypt the itemsets for security
D. Speed up disk read operations

13 Classification is a type of learning that is:

Introduction to Classification Easy
A. Unsupervised, using no labels
B. Based only on clustering
C. Used only for encryption
D. Supervised, using labeled training data

14 The output of a classification model is:

Introduction to Classification Easy
A. A continuous numeric value
B. A discrete class label
C. A compressed file
D. A sorted list of items

15 Which of the following is an example of a classification task?

Introduction to Classification Easy
A. Grouping customers without any labels
B. Predicting whether an email is spam or not spam
C. Predicting tomorrow's exact temperature in degrees
D. Sorting numbers in ascending order

16 Frequent pattern-based classification uses which of the following as features?

Frequent Pattern-Based Classification Methods Easy
A. Encrypted transaction IDs
B. Frequent patterns discovered from the data
C. Randomly generated numbers
D. Only the class labels themselves

17 An associative classifier builds classification rules using techniques adapted from methods that were originally designed to mine frequent itemsets and generate association rules from transaction databases.

Frequent Pattern-Based Classification Methods Easy
A. Linear regression
B. Image compression
C. Association rule mining
D. Sorting algorithms

18 Precision is defined as:

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Easy
A.
B.
C.
D.

19 Recall is defined as:

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Easy
A.
B.
C.
D.

20 The F1-Score is the harmonic mean of:

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Easy
A. Accuracy and Support
B. Precision and Recall
C. Lift and Confidence
D. True Positives and True Negatives

21 A transaction database has 1000 transactions. The itemset appears in 150 transactions. What is the support of this itemset?

Introduction to Association Rule Mining Medium
A.
B.
C.
D.

22 For the rule , support() and support() . What is the confidence of the rule?

Introduction to Association Rule Mining Medium
A.
B.
C.
D.

23 A rule has a lift value of . What does this indicate?

associations and correlations Medium
A. The rule has 100% confidence
B. and are negatively correlated
C. and are positively correlated
D. and are independent

24 Given support() , support() , and support() , what is the lift of ?

associations and correlations Medium
A.
B.
C.
D.

25 The Apriori algorithm relies on which key property to reduce the search space?

Introduction to Association Rule Mining Medium
A. If an itemset is frequent, all its subsets are frequent
B. All itemsets have equal support
C. Confidence always increases with itemset size
D. If an itemset is infrequent, all its supersets are frequent

26 A rule with high confidence but lift less than suggests which relationship between the antecedent and consequent?

Mining Correlation Patterns Medium
A. Perfect independence
B. Positive correlation
C. Negative correlation
D. Strong causation

27 Which correlation measure is preferred over lift because it is null-invariant (not affected by transactions containing neither item)?

Mining Correlation Patterns Medium
A. Cosine measure
B. Confidence
C. Lift
D. Support

28 In a correlation test for two items, a value significantly greater than the expected threshold indicates that the items are:

Mining Correlation Patterns Medium
A. Equally frequent
B. Correlated (dependent)
C. Mutually exclusive only
D. Independent

29 Which of the following best distinguishes classification from clustering?

Introduction to Classification Medium
A. Clustering predicts continuous values
B. Classification uses labeled training data; clustering does not
C. Both require predefined class labels
D. Classification is unsupervised; clustering is supervised

30 In a two-step classification process, what is the purpose of the first (learning) step?

Introduction to Classification Medium
A. Measure the model's accuracy
B. Build a model from the training set
C. Remove noisy attributes only
D. Apply the model to unseen data

31 Why is a separate test set used instead of evaluating a classifier on its training data?

Introduction to Classification Medium
A. To guarantee zero classification error
B. To obtain an unbiased estimate of generalization performance
C. To increase the training accuracy
D. To speed up model building

32 Associative classification builds a classifier primarily from which type of rules?

Frequent Pattern-Based Classification Methods Medium
A. Class association rules of the form
B. Unlabeled cluster centroids
C. Random decision splits
D. Regression equations

33 In frequent pattern-based classification, why are discriminative frequent patterns preferred over all frequent patterns?

Frequent Pattern-Based Classification Methods Medium
A. They eliminate the need for training data
B. They provide higher class-discriminating power as features
C. They reduce confidence to zero
D. They always have the highest support

34 The CBA (Classification Based on Associations) algorithm ranks its rules primarily by which criteria?

Frequent Pattern-Based Classification Methods Medium
A. Support, then lift
B. Rule length, then confidence
C. Alphabetical order of items
D. Confidence, then support

35 A classifier gives TP , FP , FN , TN . What is the precision?

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Medium
A.
B.
C.
D.

36 Using TP , FP , FN , TN , what is the recall?

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Medium
A.
B.
C.
D.

37 If a model has precision and recall , what is its F1-score?

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Medium
A.
B.
C.
D.

38 Why is the F1-score often preferred over accuracy for imbalanced datasets?

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Medium
A. It always equals accuracy
B. It counts true negatives most heavily
C. It ignores false negatives entirely
D. It balances precision and recall, ignoring true negatives

39 A spam filter that marks almost all emails as spam will most likely have:

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Medium
A. Low recall and low precision
B. High precision and high recall
C. High precision but low recall
D. High recall but low precision

40 A rule can be 'strong' (high support and confidence) yet misleading. Which measure best exposes this by comparing observed to expected co-occurrence?

associations and correlations Medium
A. Lift
B. Coverage
C. Support
D. Confidence

41 A transaction database has 10,000 transactions. Itemset appears in 500 transactions, item appears in 2,000, and item appears in 1,000. For the rule , what are the support and confidence respectively?

Introduction to Association Rule Mining Hard
A. Support , Confidence
B. Support , Confidence
C. Support , Confidence
D. Support , Confidence

42 For the rule with , , and , what is the lift, and what does it imply?

associations and correlations Hard
A. Lift ; and are negatively correlated
B. Lift ; and are positively correlated
C. Lift ; and are independent
D. Lift ; and are negatively correlated

43 Consider a dataset in which 90% of all transactions contain milk. A rule has confidence 0.85. What is the most accurate interpretation?

Introduction to Association Rule Mining Hard
A. The rule guarantees that 85% of milk buyers also buy bread
B. The rule is strong because confidence exceeds 0.8
C. The rule shows bread strongly implies milk since 0.85 is a high confidence
D. The rule is misleading; buying bread actually makes buying milk slightly less likely than baseline

44 The lift measure is not null-invariant. Which of the following measures is null-invariant, making it more suitable for large sparse datasets with many null transactions?

Mining Correlation Patterns Hard
A. (chi-square)
B. Lift
C. Support
D. Cosine measure

45 For two items with , , and , compute the cosine measure.

Mining Correlation Patterns Hard
A.
B.
C.
D.

46 A binary classifier produces the confusion matrix: TP = 40, FP = 10, FN = 20, TN = 30. What is the F1-score?

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Hard
A.
B.
C.
D.

47 In a highly imbalanced dataset where the positive class is only 2% of the data, a model predicts every instance as negative. Which statement is correct?

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Hard
A. Accuracy is 98% but recall for the positive class is 0
B. Accuracy is 2% and precision is undefined but recall is 100%
C. Accuracy is 98% and F1-score for the positive class is also high
D. Both accuracy and F1-score for the positive class are 98%

48 The score generalizes F1. If a fraud-detection team wants to weight recall twice as heavily as precision, which should they use?

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Hard
A.
B.
C.
D.

49 Associative classification methods such as CBA build classifiers from class association rules (CARs). What is the key difference between a CAR and a general association rule?

Frequent Pattern-Based Classification Methods Hard
A. A CAR requires confidence to always equal 1
B. A CAR has no minimum support threshold
C. A CAR uses only single-item antecedents
D. The consequent of a CAR is restricted to a class label

50 Why can discriminative frequent patterns improve classification accuracy over using single features, even though the number of frequent patterns can be huge?

Frequent Pattern-Based Classification Methods Hard
A. Frequent patterns always have higher support than individual features and are therefore more reliable
B. Frequent patterns eliminate the need for any feature selection at all
C. Frequent patterns reduce the dataset dimensionality to exactly the number of classes present
D. Frequent patterns capture feature combinations that carry more discriminative information than individual low-order features

51 In classification, why is a purely training-error-minimizing model prone to overfitting?

Introduction to Classification Hard
A. It cannot compute a decision boundary for linearly separable data
B. It requires that training and test distributions be identical
C. It always underestimates the number of classes present
D. It may model noise in the training data, reducing generalization to unseen data

52 The Apriori algorithm's efficiency relies on the downward closure property. Which statement correctly expresses this property?

Introduction to Association Rule Mining Hard
A. If an itemset is infrequent then all its subsets are infrequent
B. All supersets of a frequent itemset are also frequent
C. The support of an itemset equals the sum of its subsets' supports
D. All non-empty subsets of a frequent itemset are also frequent

53 Two items have a contingency table: both present = 400, A only = 100, B only = 100, neither = 400 (total = 1000). Testing independence with , what is the expected count for the 'both present' cell?

Mining Correlation Patterns Hard
A.
B.
C.
D.

54 The Kulczynski measure combined with imbalance ratio (IR) is often used to judge interestingness. When Kulczynski and IR is high, what does this typically indicate?

associations and correlations Hard
A. A guaranteed spurious rule that must be discarded regardless of IR
B. A neutral/uncorrelated pattern that is also strongly imbalanced between the two items
C. A strongly positively correlated and balanced pattern
D. A strongly negatively correlated pattern with balanced item frequencies

55 For a multiclass problem, macro-averaged F1 and micro-averaged F1 can differ substantially. When is micro-F1 preferable?

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Hard
A. When every class should contribute equally regardless of size
B. When you only care about the rarest minority class
C. When the dataset has exactly balanced class frequencies
D. When you want performance dominated by the more frequent classes

56 In the CMAR (Classification based on Multiple Association Rules) approach, when multiple matching rules predict different classes for a test instance, how is the conflict typically resolved?

Frequent Pattern-Based Classification Methods Hard
A. By always choosing the single rule with the highest support
B. By taking a simple unweighted majority vote of all matching rules
C. By grouping matching rules per class and using a weighted measure to select the strongest group
D. By choosing the rule that was generated earliest during mining

57 A closed frequent itemset is defined such that no immediate superset has the same support. Why are closed itemsets preferred over the full set of frequent itemsets?

Introduction to Association Rule Mining Hard
A. They guarantee all association rules will have confidence 1
B. They are always fewer than the number of maximal itemsets
C. They provide a lossless, compact representation from which all frequent itemsets and their supports can be recovered
D. They require less memory because they discard support counts entirely

58 A lazy learner like k-NN differs fundamentally from an eager learner like a decision tree in that it:

classification Hard
A. Builds a global model during training and discards the training data
B. Always achieves lower test error than eager learners
C. Defers all generalization until a query arrives, storing the training data instead of building a model
D. Cannot handle numeric attributes without discretization

59 Model X has precision 0.9, recall 0.6; Model Y has precision 0.7, recall 0.8. Comparing by F1-score, which model wins and by roughly how much?

Pattern Evaluation in Classification (Evaluation Metrics: Precision, Recall, F1-Score) Hard
A. They tie at
B. Model X wins ( vs )
C. Model Y wins ( vs )
D. Model X wins ( vs )

60 The classic 'coffee-tea' example shows that the rule can have high confidence yet lift . What is the primary lesson from this paradox?

associations and correlations Hard
A. Support must be increased to fix the contradiction between confidence and lift
B. High confidence does not imply positive correlation; correlation measures like lift are needed to detect genuine dependence
C. Confidence and lift always move in the same direction
D. Lift should never be used because it contradicts confidence
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