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Unit 4: UNSUPERVISED LEARNING: CLUSTERING AND PATTERN DETECTION - Practice Quiz

INT234 — Predictive Analytics 50 Questions
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1 Which of the following best defines Unsupervised Learning?

A. Learning where the data has no predefined labels or target variables
B. Learning where the model predicts a categorical target variable
C. Learning where the model is rewarded or punished based on actions
D. Learning where the model predicts a continuous target variable

2 In K-Means clustering, what does 'K' represent?

A. The number of features in the dataset
B. The number of iterations
C. The number of clusters the algorithm forms
D. The distance metric used

3 What is the primary objective function that K-Means minimizes?

A. Inter-cluster distance
B. The Silhouette coefficient
C. Within-Cluster Sum of Squares (WCSS)
D. The number of outliers

4 Which of the following is the first step in the standard K-Means algorithm?

A. Assign points to the nearest cluster
B. Calculate the centroid of all points
C. Randomly initialize K centroids
D. Calculate the total variance

5 The 'Random Initialization Trap' in K-Means refers to:

A. The inability to handle categorical data
B. Different initial centroid positions leading to different, suboptimal results
C. The algorithm running indefinitely without convergence
D. The algorithm selecting the wrong number of K

6 Which technique is commonly used to mitigate the Random Initialization Trap?

A. Agglomerative Clustering
B. Gradient Descent
C. Principal Component Analysis
D. K-Means++

7 What is the 'Elbow Method' used for in K-Means clustering?

A. Visualizing high-dimensional data
B. Calculating the distance between centroids
C. Stopping the algorithm early
D. Determining the optimal number of clusters

8 In an Elbow Method plot, what variable is typically on the Y-axis?

A. Computation Time
B. Number of Clusters (K)
C. WCSS (Inertia)
D. Accuracy

9 When does the K-Means algorithm stop iterating?

A. When the centroids no longer move significantly between iterations
B. When the number of clusters equals the number of data points
C. When the WCSS becomes zero
D. When every point is in its own cluster

10 Which of the following is a limitation of K-Means clustering?

A. It can only handle binary data
B. It requires the number of clusters to be specified in advance
C. It always finds the global optimum
D. It is computationally expensive for small datasets

11 Agglomerative Hierarchical Clustering is best described as a approach.

A. Centroid-based
B. Density-based
C. Top-down
D. Bottom-up

12 Divisive Hierarchical Clustering is best described as a approach.

A. Randomized
B. Grid-based
C. Bottom-up
D. Top-down

13 What diagram is commonly used to visualize Hierarchical Clustering?

A. Scatter plot
B. Box plot
C. Dendrogram
D. Histogram

14 In a dendrogram, the vertical axis typically represents:

A. The Euclidean distance or dissimilarity between clusters
B. The time taken to cluster
C. The frequency of data points
D. The number of clusters

15 How do you determine the optimal number of clusters using a dendrogram?

A. It is impossible to determine K from a dendrogram
B. Choose the height where the first merge occurs
C. Count the number of leaves at the bottom
D. Cut the dendrogram at the point with the longest vertical distance without crossing horizontal lines

16 Which linkage method defines the distance between two clusters as the shortest distance between any single point in one cluster and any single point in the other?

A. Ward's Method
B. Complete Linkage
C. Average Linkage
D. Single Linkage

17 Which linkage method defines the distance between two clusters as the maximum distance between any point in the first cluster and any point in the second?

A. Centroid Linkage
B. Single Linkage
C. Complete Linkage
D. Average Linkage

18 Average Linkage calculates the distance between clusters by:

A. Using the minimum distance between points
B. Taking the average of all pairwise distances between points in the two clusters
C. Using the distance between the centroids of the clusters
D. Taking the median distance of all points

19 Centroid Linkage measures the distance between clusters based on:

A. The distance between the geometric centers (means) of the clusters
B. The furthest points in the clusters
C. The sum of squared errors
D. The closest points in the clusters

20 Which linkage method is most notorious for producing the 'chaining' effect (long, stringy clusters)?

A. Average Linkage
B. Complete Linkage
C. Single Linkage
D. Ward's Method

21 What is a primary advantage of Hierarchical Clustering over K-Means?

A. It does not require assuming the number of clusters (K) beforehand
B. It works better with high-dimensional data
C. It always uses Manhattan distance
D. It is computationally faster on large datasets

22 Market Basket Analysis is a specific application of which technique?

A. Association Rule Learning
B. Clustering
C. Linear Regression
D. Decision Trees

23 In the rule {Bread} -> {Butter}, {Bread} is the:

A. Lift
B. Consequent
C. Support
D. Antecedent

24 What does the metric 'Support' measure in Association Rules?

A. The reliability of the rule
B. The ratio of the rule's confidence to the expected confidence
C. The frequency with which an itemset appears in the dataset
D. The correlation between items

25 How is 'Confidence' for the rule A -> B calculated?

A. Support(A & B) / Support(B)
B. Support(B) / Support(A)
C. Support(A) / Support(B)
D. Support(A & B) / Support(A)

26 What does a 'Lift' value greater than 1 indicate?

A. The rule is invalid
B. The presence of the antecedent increases the likelihood of the consequent
C. The items are substitutes (negatively correlated)
D. The items are independent of each other

27 If the Lift of a rule A -> B is exactly 1, what does this imply?

A. A and B are independent
B. A and B are never bought together
C. The confidence is 100%
D. A and B are perfectly correlated

28 Which algorithm is most commonly associated with mining frequent itemsets for Association Rules?

A. Naive Bayes
B. Random Forest
C. K-Nearest Neighbors
D. Apriori

29 The Apriori algorithm uses the 'Downward Closure Property'. What does this property state?

A. All supersets of a frequent itemset must be frequent
B. If an itemset is infrequent, its subsets must be frequent
C. Support always equals Confidence
D. All subsets of a frequent itemset must be frequent

30 Which of the following is NOT a step in the K-Means algorithm?

A. Update of centroids to the mean of assigned points
B. Initialization of centroids
C. Assignment of points to the nearest centroid
D. Calculation of distance matrix for all pairs of points

31 What is the main reason to scale features (normalize/standardize) before running K-Means?

A. To prevent features with large magnitudes from dominating distance calculations
B. To make the data normally distributed
C. To convert categorical variables to numeric
D. To increase the number of clusters

32 In the context of Association Rules, the 'Consequent' is found on which side of the arrow?

A. Both sides
B. Left (IF side)
C. Right (THEN side)
D. Neither side

33 Which metric would you look at to determine if a high-confidence rule is merely a coincidence because the consequent is very popular?

A. Lift
B. Support
C. Confidence
D. Accuracy

34 If Support(A) = 0.4 and Support(A, B) = 0.2, what is the Confidence(A -> B)?

A. 0.2
B. 2.0
C. 0.8
D. 0.5

35 K-Means is sensitive to which of the following?

A. Scaling
B. Outliers
C. Redundant features
D. All of the above

36 Which clustering method generates a hierarchy of clusters?

A. Density-Based Clustering
B. Grid-Based Clustering
C. Partitioning Clustering (K-Means)
D. Hierarchical Clustering

37 In the Silhouette Analysis for K-Means, a score close to +1 indicates:

A. The point is well-matched to its own cluster and far from neighboring clusters
B. The point is overlapping with other clusters
C. The point is assigned to the wrong cluster
D. The point is an outlier

38 Which of the following scenarios is ideal for K-Means clustering?

A. Clusters are non-spherical and have irregular shapes
B. Clusters contain a lot of noise and outliers
C. Clusters are spherical and distinct
D. Clusters are of varying densities

39 In Association Rule Mining, a 'Frequent Itemset' is an itemset whose support is:

A. Greater than the confidence threshold
B. Equal to 1
C. Less than a minimum support threshold
D. Greater than or equal to a minimum support threshold

40 Which distance metric is most commonly used in K-Means?

A. Euclidean Distance
B. Manhattan Distance
C. Cosine Similarity
D. Hamming Distance

41 Which linkage method in Hierarchical Clustering aims to minimize the variance within clusters being merged (similar to K-Means)?

A. Average Linkage
B. Single Linkage
C. Complete Linkage
D. Ward's Method

42 What is a 'hard' clustering assignment?

A. A data point belongs to multiple clusters with varying probabilities
B. A data point belongs to exactly one cluster
C. The algorithm is hard to implement
D. The clustering is performed on hardware

43 In Market Basket Analysis, if {Milk, Bread} -> {Eggs} has a confidence of 0.7, it means:

A. 70% of all transactions contain Eggs
B. 70% of transactions containing Milk and Bread also contain Eggs
C. Eggs are bought 70% more often with Milk and Bread than expected
D. 70% of customers buy Milk and Bread

44 Which of the following is an application of Clustering?

A. Predicting house prices
B. Customer Segmentation
C. Predicting credit default
D. Classifying emails as spam or not spam

45 In the context of K-Means, what is a Centroid?

A. The outlier point in a cluster
B. The point closest to the origin
C. The boundary of the cluster
D. The arithmetic mean position of all the points in the cluster

46 Which of the following is true regarding the computational complexity of Hierarchical Clustering compared to K-Means for large datasets?

A. They have the exact same complexity
B. Hierarchical is generally slower and more memory intensive
C. Hierarchical cannot run on large datasets
D. Hierarchical is generally faster

47 When performing K-Means, if you initialize centroids to the same location, what happens?

A. It automatically separates them
B. The algorithm works perfectly
C. The algorithm converges in one step
D. The algorithm fails to generate distinct clusters

48 A Lift value of 0.5 suggests:

A. Positive correlation
B. Strong rule
C. Negative correlation (Substitutes)
D. Independence

49 What happens to the WCSS as the number of clusters (K) increases towards the total number of data points?

A. It remains constant
B. It increases
C. It decreases towards zero
D. It fluctuates randomly

50 Which step ensures that the K-Means algorithm converges?

A. The use of Manhattan distance
B. The randomization of initial points
C. The use of the Elbow method
D. The fact that WCSS decreases or stays the same with every iteration
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