1 $Which of the following best defines Unsupervised Learning?$

A.

Learning where the model predicts a categorical target variable

B.

Learning where the model predicts a continuous target variable

C.

Learning where the data has no predefined labels or target variables

D.

Learning where the model is rewarded or punished based on actions

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

A.

The distance metric used

B.

The number of clusters the algorithm forms

C.

The number of iterations

D.

The number of features in the dataset

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

A.

The number of outliers

B.

Inter-cluster distance

C.

Within-Cluster Sum of Squares (WCSS)

D.

The Silhouette coefficient

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

A.

Calculate the centroid of all points

B.

Randomly initialize K centroids

C.

Assign points to the nearest cluster

D.

Calculate the total variance

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

A.

The inability to handle categorical data

B.

The algorithm selecting the wrong number of K

C.

The algorithm running indefinitely without convergence

D.

Different initial centroid positions leading to different, suboptimal results

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

A.

Gradient Descent

B.

Agglomerative Clustering

C.

Principal Component Analysis

D.

K-Means++

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

A.

Calculating the distance between centroids

B.

Determining the optimal number of clusters

C.

Visualizing high-dimensional data

D.

Stopping the algorithm early

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

A.

WCSS (Inertia)

B.

Computation Time

C.

Number of Clusters (K)

D.

Accuracy

9 $When does the K-Means algorithm stop iterating?$

A.

When the number of clusters equals the number of data points

B.

When every point is in its own cluster

C.

When the WCSS becomes zero

D.

When the centroids no longer move significantly between iterations

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

A.

It can only handle binary data

B.

It is computationally expensive for small datasets

C.

It requires the number of clusters to be specified in advance

D.

It always finds the global optimum

11 $Agglomerative Hierarchical Clustering is best described as a approach.$

A.

Centroid-based

B.

Top-down

C.

Density-based

D.

Bottom-up

12 $Divisive Hierarchical Clustering is best described as a approach.$

A.

Top-down

B.

Randomized

C.

Bottom-up

D.

Grid-based

13 $What diagram is commonly used to visualize Hierarchical Clustering?$

A.

Dendrogram

B.

Histogram

C.

Scatter plot

D.

Box plot

14 $In a dendrogram, the vertical axis typically represents:$

A.

The frequency of data points

B.

The time taken to cluster

C.

The number of clusters

D.

The Euclidean distance or dissimilarity between 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.

Count the number of leaves at the bottom

C.

Choose the height where the first merge occurs

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.

Single Linkage

D.

Average 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.

Single Linkage

B.

Complete Linkage

C.

Average Linkage

D.

Centroid Linkage

18 $Average Linkage calculates the distance between clusters by:$

A.

Taking the average of all pairwise distances between points in the two clusters

B.

Using the minimum distance between points

C.

Taking the median distance of all points

D.

Using the distance between the centroids of the clusters

19 $Centroid Linkage measures the distance between clusters based on:$

A.

The closest points in the clusters

B.

The furthest points in the clusters

C.

The distance between the geometric centers (means) of the clusters

D.

The sum of squared errors

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

A.

Ward's Method

B.

Single Linkage

C.

Average Linkage

D.

Complete Linkage

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

A.

It works better with high-dimensional data

B.

It always uses Manhattan distance

C.

It does not require assuming the number of clusters (K) beforehand

D.

It is computationally faster on large datasets

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

A.

Decision Trees

B.

Association Rule Learning

C.

Clustering

D.

Linear Regression

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

A.

Antecedent

B.

Consequent

C.

Lift

D.

Support

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

A.

The reliability of the rule

B.

The correlation between items

C.

The frequency with which an itemset appears in the dataset

D.

The ratio of the rule's confidence to the expected confidence

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

A.

Support(A) / Support(B)

B.

Support(A & B) / Support(A)

C.

Support(B) / Support(A)

D.

Support(A & B) / Support(B)

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

A.

The items are substitutes (negatively correlated)

B.

The presence of the antecedent increases the likelihood of the consequent

C.

The items are independent of each other

D.

The rule is invalid

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

A.

The confidence is 100%

B.

A and B are independent

C.

A and B are never bought together

D.

A and B are perfectly correlated

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

A.

K-Nearest Neighbors

B.

Apriori

C.

Random Forest

D.

Naive Bayes

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

A.

All subsets of a frequent itemset must be frequent

B.

Support always equals Confidence

C.

If an itemset is infrequent, its subsets must be frequent

D.

All supersets of a frequent itemset must be frequent

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

A.

Assignment of points to the nearest centroid

B.

Calculation of distance matrix for all pairs of points

C.

Update of centroids to the mean of assigned points

D.

Initialization of centroids

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

A.

To increase the number of clusters

B.

To convert categorical variables to numeric

C.

To prevent features with large magnitudes from dominating distance calculations

D.

To make the data normally distributed

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

A.

Right (THEN side)

B.

Neither side

C.

Left (IF side)

D.

Both sides

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.

Accuracy

B.

Support

C.

Lift

D.

Confidence

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.

0.5

D.

0.8

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

A.

Outliers

B.

Redundant features

C.

All of the above

D.

Scaling

36 $Which clustering method generates a hierarchy of clusters?$

A.

Grid-Based Clustering

B.

Hierarchical Clustering

C.

Density-Based Clustering

D.

Partitioning Clustering (K-Means)

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 contain a lot of noise and outliers

B.

Clusters are non-spherical and have irregular shapes

C.

Clusters are of varying densities

D.

Clusters are spherical and distinct

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.

Manhattan Distance

B.

Cosine Similarity

C.

Euclidean Distance

D.

Hamming Distance

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

A.

Single Linkage

B.

Complete Linkage

C.

Ward's Method

D.

Average Linkage

42 $What is a 'hard' clustering assignment?$

A.

The algorithm is hard to implement

B.

A data point belongs to exactly one cluster

C.

The clustering is performed on hardware

D.

A data point belongs to multiple clusters with varying probabilities

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

A.

Eggs are bought 70% more often with Milk and Bread than expected

B.

70% of transactions containing Milk and Bread also contain Eggs

C.

70% of customers buy Milk and Bread

D.

70% of all transactions contain Eggs

44 $Which of the following is an application of Clustering?$

A.

Predicting house prices

B.

Customer Segmentation

C.

Classifying emails as spam or not spam

D.

Predicting credit default

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

A.

The arithmetic mean position of all the points in the cluster

B.

The boundary of the cluster

C.

The point closest to the origin

D.

The outlier point in a cluster

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

A.

Hierarchical cannot run on large datasets

B.

Hierarchical is generally slower and more memory intensive

C.

Hierarchical is generally faster

D.

They have the exact same complexity

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

A.

The algorithm fails to generate distinct clusters

B.

The algorithm converges in one step

C.

The algorithm works perfectly

D.

It automatically separates them

48 $A Lift value of 0.5 suggests:$

A.

Positive correlation

B.

Negative correlation (Substitutes)

C.

Independence

D.

Strong rule

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

A.

It increases

B.

It decreases towards zero

C.

It remains constant

D.

It fluctuates randomly

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

A.

The use of the Elbow method

B.

The randomization of initial points

C.

The use of Manhattan distance

D.

The fact that WCSS decreases or stays the same with every iteration

Unit 4 - Practice Quiz