Answer-first summary for fast verification
Answer: k-Means Clustering
**Correct Answer: k-Means Clustering.** For tasks that involve unsupervised clustering, where the objective is to group similar data points without predefined labels, k-Means Clustering stands out as the appropriate algorithm. It works by partitioning the data into 'k' distinct clusters based on feature similarity, striving to minimize the variance within each cluster. This method is widely utilized for various applications, including customer segmentation and organizing similar items. Given its support in Databricks MLlib, k-Means Clustering is a practical choice for your project's clustering needs.
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Your team is embarking on a machine learning project focused on clustering tasks, aiming to group similar data points together. Among the algorithms supported by Databricks MLlib, which one is specifically designed for unsupervised clustering tasks?
A
Decision Trees
B
Support Vector Machines
C
k-Means Clustering
D
Naive Bayes
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