Learn the difference between clustering and classification algorithms, and why K-Means is a clustering algorithm while Two-Class Logistic Regression and Neural Network are classification algorithms.
Table of Contents
Question
Which of the following is a clustering algorithm?
A. Two-Class Logistic Regression
B. Two-Class Neural Network
C. K-Means
Answer
C. K-Means
Explanation
K-Means is a clustering algorithm.
The correct answer is C. K-Means. K-Means is a clustering algorithm that partitions the data into a specified number of clusters, based on the similarity of the data points. Clustering algorithms are a type of unsupervised learning, which means they do not require labeled data to find patterns or groups in the data. Clustering algorithms are useful for exploratory data analysis, feature engineering, anomaly detection, and customer segmentation.
Two-Class Logistic Regression and Two-Class Neural Network are not clustering algorithms, but classification algorithms. Classification algorithms are a type of supervised learning, which means they require labeled data to learn how to predict the class or category of a new data point. Classification algorithms are useful for binary or multi-class problems, such as spam detection, sentiment analysis, or image recognition.
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