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Explanation

K-means clustering is an unsupervised learning algorithm used for data partitioning. Here's why it's the correct answer:

1. Unsupervised Learning: K-means clustering is a classic example of unsupervised learning where the algorithm groups data points into clusters without any pre-labeled training data.

2. Data Partitioning: The algorithm partitions data into 'k' clusters based on similarity, where each data point belongs to the cluster with the nearest mean (centroid).

3. No Pre-classified Training Sample: Unlike supervised learning algorithms (such as classification algorithms), k-means doesn't require labeled training data. It discovers patterns and structures in the data on its own.

4. Other options analysis:

   • A) Frequent pattern growth: This is an association rule mining algorithm (like Apriori) for finding frequent itemsets, not primarily for data partitioning.
   • C) Sampling: This is a statistical technique for selecting subsets of data, not an unsupervised learning algorithm for partitioning.
   • D) Negative association: This refers to association rules with negative correlations, not a partitioning algorithm.
   • E) Frequent pattern tree: This is another association rule mining algorithm (FP-Growth), similar to frequent pattern growth.

K-means clustering is widely used in data mining for customer segmentation, document clustering, image compression, and other applications where natural groupings in data need to be discovered.