What is “clustering” in unsupervised learning?
a) Grouping similar data points together without labels
b) Predicting the next data point in a sequence
c) Classifying data based on predefined categories
d) Reinforcing learning through feedback
Answer:
a) Grouping similar data points together without labels
Explanation:
Clustering is an unsupervised learning technique where the goal is to group similar data points together based on their features. Since the data is unlabeled, the algorithm must discover patterns and relationships within the data without supervision.
Common clustering algorithms include k-means, hierarchical clustering, and DBSCAN. These methods are used in applications such as customer segmentation, image analysis, and anomaly detection.
Clustering is valuable in exploratory data analysis, where identifying hidden structures in data can lead to insights that inform further decision-making and research.