Match the following \(\begin{array}{clcl} & \textbf{List – I} & & \textbf{List…
2014
Match the following
\(\begin{array}{clcl} & \textbf{List – I} & & \textbf{List – II} \\ \text{a.} & \text{Classification} & \text{i.} & \text{Principal component analysis} \\ \text{b.} & \text{Clustering } & \text{ii.} & \text{Branch and Bound} \\ \text{c.} & \text{Feature Extraction} & \text{iii.} & \text{K-nearest neighbour} \\ \text{d.} & \text{Feature Selection} & \text{iv.} & \text{K-means} \\ \end{array}\)
Codes :
- A.
a-iii; b-iv; c-ii; d-i
- B.
a-iv; b-iii; c-i; d-ii
- C.
a-iii; b-iv; c-i; d-ii
- D.
a-iv; b-iii; c-ii; d-i
Attempted by 56 students.
Show answer & explanation
Correct answer: C
Correct matching: a → K-nearest neighbour; b → K-means; c → Principal component analysis; d → Branch and Bound
Classification corresponds to K-nearest neighbour: K-nearest neighbour is a supervised learning algorithm that assigns a class label based on the labels of nearby training examples.
Clustering corresponds to K-means: K-means is an unsupervised algorithm that groups unlabeled data into clusters by minimizing intra-cluster variance.
Feature Extraction corresponds to Principal Component Analysis: PCA creates new features (principal components) that are linear combinations of original features and capture the most variance.
Feature Selection corresponds to Branch and Bound: Branch and Bound is a search/optimization method used to select a subset of the original features without creating new combined features.
Summary: Feature extraction (PCA) creates transformed features; feature selection (Branch and Bound) picks a subset of existing features. Classification uses supervised methods like KNN; clustering uses unsupervised methods like K-means.