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Unit 1 - Practice Quiz

INT423

1 What is the primary characteristic of Unsupervised Learning?

A. The algorithm trains on labeled data
B. The algorithm trains on data without labels
C. The algorithm uses a feedback loop for rewards
D. The algorithm predicts a continuous numerical value

2 Which of the following is a primary goal of clustering?

A. To predict future values based on past trends
B. To group similar data points together
C. To classify images into predefined categories
D. To reduce the noise in a signal

3 In the context of K-Means, what does 'K' represent?

A. The number of data points
B. The number of iterations
C. The number of clusters
D. The dimension of the features

4 What kind of problem is K-Means designed to solve?

A. Regression
B. Classification
C. Clustering
D. Reinforcement Learning

5 What is a 'centroid' in the K-Means algorithm?

A. An outlier in the dataset
B. The geometric center of a cluster
C. The boundary line between clusters
D. The data point furthest from the center

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

A. Manhattan distance
B. Euclidean distance
C. Cosine similarity
D. Hamming distance

7 What is the first step of the K-Means algorithm?

A. Assign points to the nearest cluster
B. Update the centroids
C. Initialize cluster centroids
D. Calculate the total error

8 During the assignment step of K-Means, how is a data point assigned to a cluster?

A. To the cluster with the most points
B. To the cluster with the closest centroid
C. Randomly
D. To the cluster with the highest variance

9 What happens during the update step of the K-Means algorithm?

A. New data points are added
B. Centroids are moved to the mean of their assigned points
C. The number of clusters (K) is increased
D. Points are reassigned to different clusters

10 When does the K-Means algorithm stop iterating?

A. When the centroids do not change significantly
B. After exactly 10 iterations
C. When the training error is zero
D. When K is equal to N

11 What is the optimization objective (cost function) of K-Means?

A. Maximize Inter-cluster distance
B. Minimize Within-Cluster Sum of Squares (WCSS)
C. Maximize the Silhouette score
D. Minimize the number of clusters

12 The objective function of K-Means is non-convex. What does this imply?

A. It always finds the global minimum
B. It may get stuck in a local minimum
C. It cannot be optimized
D. It requires labeled data

13 Which of the following is a disadvantage of the K-Means algorithm?

A. It is computationally very expensive for small datasets
B. It is sensitive to outliers
C. It works only on labeled data
D. It cannot handle numerical data

14 If you set K equal to the number of data points (N), what will the WCSS be?

A. Infinity
B. Zero
C. Maximum possible value
D. Undefined

15 What is the 'Elbow Method' used for?

A. Initializing centroids
B. Speeding up convergence
C. Determining the optimal number of clusters (K)
D. Handling outliers

16 In the Elbow Method plot, what is typically on the Y-axis?

A. Number of clusters (K)
B. Accuracy
C. Inertia or WCSS
D. Time taken

17 What is the 'Random Initialization Trap' in K-Means?

A. Choosing K randomly leads to errors
B. Randomly picking centroids can lead to poor local optima
C. Random data points cannot be clustered
D. The algorithm fails if data is random

18 What is K-Means++?

A. A version of K-Means for supervised learning
B. A method to choose the optimal K
C. A smarter initialization technique for K-Means
D. A post-processing step for K-Means

19 How does K-Means++ select the first centroid?

A. It calculates the global mean
B. It chooses the point furthest from the origin
C. It picks one data point uniformly at random
D. It picks the point with the highest variance

20 What is the difference between Hard Clustering and Soft Clustering?

A. Hard clustering is faster; Soft is slower
B. Hard clustering allows overlapping; Soft does not
C. Hard clustering assigns a point to one cluster; Soft assigns probabilities
D. Hard clustering uses K-Means; Soft uses Decision Trees

21 Standard K-Means is an example of which type of clustering?

A. Soft Clustering
B. Hard Clustering
C. Hierarchical Clustering
D. Density-based Clustering

22 Which algorithm is a well-known example of Soft Clustering?

A. K-Means
B. Fuzzy C-Means
C. DBSCAN
D. Agglomerative Clustering

23 If a data point has a membership vector [0.7, 0.2, 0.1] for 3 clusters, this is an example of:

A. Hard Clustering
B. Soft Clustering
C. Regression
D. Outlier Detection

24 What shape of clusters does K-Means typically assume?

A. Arbitrary shapes
B. Spherical or convex
C. Elongated shapes
D. Spirals

25 Why is feature scaling (standardization/normalization) important in K-Means?

A. It is not important
B. To ensure the algorithm runs faster only
C. To prevent features with larger ranges from dominating the distance metric
D. To convert categorical data to numerical

26 What is the computational complexity of one iteration of K-Means?

A. O(N^2)
B. O(K N d)
C. O(e^N)
D. O(N * log N)

27 In the Elbow method, the 'elbow' point represents:

A. The point of maximum error
B. The point where adding another cluster does not significantly reduce WCSS
C. The point where WCSS becomes zero
D. The point where K equals 1

28 Which of the following implies that K-Means has converged?

A. The assignment of points to clusters remains unchanged
B. WCSS increases
C. The number of clusters decreases
D. The data becomes labeled

29 What is 'Inertia' in the context of Scikit-Learn's K-Means implementation?

A. The time taken to run
B. The sum of squared distances of samples to their closest cluster center
C. The distance between cluster centers
D. The number of iterations

30 Which strategy is used to mitigate the local optima problem in K-Means?

A. Decrease the learning rate
B. Run the algorithm multiple times with different initializations
C. Increase the number of clusters
D. Use Manhattan distance

31 Can K-Means handle categorical data directly?

A. Yes, it works natively
B. No, it requires numerical data
C. Only if the data is ordinal
D. Yes, using Hamming distance

32 In K-Means++, how is the probability of selecting the next centroid determined?

A. Inversely proportional to the distance from existing centroids
B. Proportional to the squared distance from the nearest existing centroid
C. Randomly with uniform distribution
D. Based on the density of the points

33 What is a 'Voronoi Diagram' in relation to K-Means?

A. A plot of the cost function
B. A visualization where regions are defined by the closest centroid
C. A method to initialize K
D. A type of soft clustering

34 If the clusters in the data are of very different densities and sizes, K-Means will:

A. Perform perfectly
B. Likely fail to identify the correct clusters
C. Automatically adjust the metric
D. Merge the clusters

35 Which step ensures K-Means is an unsupervised algorithm?

A. Calculating the mean
B. Not using target labels for training
C. Iterating until convergence
D. Minimizing WCSS

36 In the equation for WCSS, what is being squared?

A. The number of clusters
B. The distance between a point and its assigned centroid
C. The distance between two centroids
D. The number of iterations

37 Why is it often difficult to pick the optimal K using the Elbow method?

A. The plot is always a straight line
B. The 'elbow' might not be sharp or clear
C. It requires labeled data
D. It takes too long to compute

38 What is the primary role of the 'Coordinate Descent' concept in K-Means?

A. It is the method used to optimize the objective function
B. It is used for initialization
C. It is used to visualize data
D. It calculates the distance

39 If you perform K-Means on a dataset with 2 distinct well-separated blobs but set K=4, what happens?

A. The algorithm crashes
B. It finds 2 clusters and ignores the other 2
C. It splits the natural blobs into smaller clusters
D. It merges the blobs

40 In Soft Clustering, the sum of membership weights for a single data point across all clusters usually equals:

A.
B. 1
C. 100
D. K

41 Which of the following is NOT an application of K-Means?

A. Customer Segmentation
B. Image Compression (Color Quantization)
C. Spam Classification (Supervised)
D. Document Clustering

42 Does K-Means guarantee finding the global optimum for the WCSS?

A. Yes, always
B. No, it depends on initialization
C. Yes, if K is small
D. Only if using Manhattan distance

43 The computational cost of the distance calculation step for one point against K centroids is proportional to:

A. K
B. N
C. N^2
D. 1

44 Which component constitutes the 'model' after training K-Means?

A. The original dataset
B. The coordinates of the final centroids
C. The list of outliers
D. The Elbow plot

45 What is the relationship between Within-Cluster variance and Between-Cluster variance in a good clustering?

A. High within-cluster, Low between-cluster
B. Low within-cluster, High between-cluster
C. High within-cluster, High between-cluster
D. Low within-cluster, Low between-cluster

46 Lloyd's Algorithm is another name for:

A. K-Means Algorithm
B. Hierarchical Clustering
C. DBSCAN
D. KNN

47 In the context of image segmentation, what does a pixel represent in K-Means?

A. A cluster
B. A centroid
C. A data point
D. A label

48 Why might one choose a K value slightly different from the Elbow point?

A. To increase computational cost
B. Based on business requirements or downstream tasks
C. Because the Elbow method is always wrong
D. To maximize WCSS

49 If K=1, the centroid location will be:

A. The origin (0,0)
B. The mean of the entire dataset
C. A random data point
D. Undefined

50 What happens if a cluster becomes empty during K-Means iterations?

A. The algorithm stops
B. The empty cluster is usually re-initialized or removed
C. The K value increases
D. It is ignored and WCSS becomes 0