Business Intelligence and Analytics Nptel Week 9 Answers
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Nptel Business Intelligence and Analytics Week 9 Answers (Jan-Apr 2025)
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1) Which of the following statements is NOT true about clustering algorithms?
a. K-medoids algorithm uses actual data points as cluster representatives, while K-modes algorithm employs modes to assess similarity in categorical data.
b. K-means algorithm calculates the mean of points within a cluster to determine the centroid, while K-modes algorithm utilizes modes to evaluate similarity in categorical data.
c. K-medoids algorithm is generally more robust to outliers and noise compared to K-means algorithm.
d. The K-means algorithm always produces better results than K-medoids for all types of datasets.
2) Which of the following statements is true about the agglomerative hierarchical clustering method?
a. Agglomerative hierarchical clustering follows a top-down approach, starting with all objects in one cluster and splitting them iteratively.
b. In agglomerative hierarchical clustering, each data point starts as an individual cluster, and clusters are merged iteratively.
c. The merging process in agglomerative clustering is random and does not depend on distance measures.
d. Agglomerative hierarchical clustering requires exactly 𝑛 + 1 iterations to form the final clustering structure.
3) Which hierarchical clustering method computes all pairwise dissimilarities between the observations in cluster A and the observations in cluster B, and records the smallest of these dissimilarities?
a. Single linkage
b. Average linkage
c. Complete linkage
d. Centroid linkage
4) A dendrogram in hierarchical clustering is a __________ representation that shows how clusters are merged at different levels.
a. Linear
b. Tree-like
c. Tabular
d. Circular
5) ________ is an unsupervised learning algorithm that groups data points into clusters based on similarity.
a. Linear Regression
b. K-Means Clustering
c. Decision Tree
d. Logistic Regression
Nptel Business Intelligence and Analytics Week 9 Answers
6) A data scientist is working with a dataset where the number of fraudulent transactions is significantly lower than the number of legitimate transactions. Which technique would be most suitable to handle this class imbalance?
a. PCA
b. SMOTE
c. Decision Tree
d. t-SNE
7) In a 3-dimensional space represented by coordinates (x, y, z), two cluster centroids, A and B, have coordinates A(1, 5, 8) and B (7, 3, 2). Calculate the Euclidean distance between these centroids to determine their dissimilarity. Round your answer to two decimal places.
a. 8.72 units
b. 7.11 units
c. 8.54 units
d. 9.38 units
8) The elbow method in K-means clustering is commonly used to:
a. Identify the convergence threshold
b. Optimize the starting centroids
c. Determine the ideal number of clusters
d. Choose the distance metric
Nptel Business Intelligence and Analytics Week 9 Answers
9) What will be the Manhattan distance for observation (8, 8) from cluster centroid C1 in the second iteration?
a. 12
b. 8
c. 10
d. 14
10) A dendrogram is used in __________ clustering to visualize the merging of clusters.
a. Hierarchical
b. K-Means
c. DBSCAN
d. Spectral
11) __________ are used to determine how distances between clusters are measured in hierarchical clustering.
a. Partitioning methods
b. Linkage measures
c. Cross-validation
d. Density measures
Nptel Business Intelligence and Analytics Week 9 Answers
12) Which of the following best describes the divisive hierarchical clustering method?
a. Probabilistic
b. Deterministic
c. Stochastic
d. Non-parametric
13) Density-based clustering methods group data points based on density, requiring that each core point’s neighborhood within a specified radius contains at least a minimum number of points.
a. True
b. False
14) The clustering objective function seeks to achieve which of the following?
a. High similarity within clusters, high similarity between clusters
b. Low similarity within clusters, low similarity between clusters
c. High similarity within clusters, low similarity between clusters
d. Low similarity within clusters, high similarity between clusters
15) The k-modes method is a variant of k-means that is specifically used for clustering:
a. Numerical data
b. Sequential data
c. Nominal data
d. Time-series data
Nptel Business Intelligence and Analytics Week 9 Answers
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