A researcher is working on a project to analyze the topics and themes in a large collection of news articles. The researcher wants to automatically group similar articles together without any prior knowledge of the underlying topics. Which unsupervised learning algorithm would be most suitable for this task?a)Principal Component Analysis (PCA)b)Linear Regressionc)Naive Bayesd)Hierarchical Clustering
Question
A researcher is working on a project to analyze the topics and themes in a large collection of news articles. The researcher wants to automatically group similar articles together without any prior knowledge of the underlying topics. Which unsupervised learning algorithm would be most suitable for this task?a)Principal Component Analysis (PCA)b)Linear Regressionc)Naive Bayesd)Hierarchical Clustering
Solution
The most suitable unsupervised learning algorithm for this task would be d) Hierarchical Clustering.
Here's why:
Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. In this case, the researcher does not have any prior knowledge of the topics in the news articles.
Hierarchical Clustering is an algorithm that builds a hierarchy of clusters where each node is a cluster consisting of the clusters of its daughter nodes. This would allow the researcher to group similar articles together based on their topics and themes.
a) Principal Component Analysis (PCA) is not suitable because it is a dimensionality reduction method, not a clustering algorithm.
b) Linear Regression is a supervised learning algorithm used for predicting a continuous outcome variable (also called the dependent variable) based on one or more predictor variables (also called independent variables).
c) Naive Bayes is a supervised learning method used for classification tasks, not for clustering.
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