Answer-first summary for fast verification
Answer: k-Means Clustering
**k-Means Clustering** is the correct choice for unsupervised clustering tasks. This algorithm efficiently partitions data into k clusters by minimizing variance within each cluster, making it ideal for applications like customer segmentation or grouping similar items. Databricks MLlib's support for k-Means Clustering ensures its suitability for your project's needs.
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In your machine learning project, you're tasked with grouping similar data points together through clustering. Which of the following algorithms, supported by Databricks MLlib, is designed for unsupervised clustering tasks?
A
Support Vector Machines
B
k-Means Clustering
C
Decision Trees
D
Naive Bayes
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