Within common data mining categories, which technique is considered unsupervised because it discovers structure in unlabeled data without target variables? Select the single best answer.

Introduction / Context:
Data mining tasks are often divided into supervised (predictive) and unsupervised (descriptive) learning. Supervised methods learn from labeled examples to predict a target (e.g., churn yes/no or sales amount), while unsupervised methods discover natural groupings and patterns in data without predefined labels.

Given Data / Assumptions:

• No explicit target variable for unsupervised learning.
• Candidate techniques: clustering, regression, and RFM analysis.
• Goal: identify the method that is inherently unsupervised.

Concept / Approach:

Cluster analysis (e.g., k-means, hierarchical clustering, DBSCAN) is unsupervised: it partitions records into groups based on similarity. Regression is typically supervised (linear, logistic, etc.), mapping features to a known numeric or categorical target. RFM is a business segmentation heuristic using Recency, Frequency, and Monetary ranks; it is descriptive rather than a canonical machine-learning algorithm and is not the standard example of unsupervised learning compared to clustering.

Step-by-Step Solution:

Verification / Alternative check:

Introductory ML texts list clustering and association rules as primary unsupervised methods; regression appears in supervised chapters.

Why Other Options Are Wrong:

Regression only: supervised by definition. RFM only: heuristic segmentation, not a standard unsupervised algorithm. Both Regression and RFM: mixes supervised with descriptive ranking; incorrect.

Common Pitfalls:

Assuming logistic regression is unsupervised because it outputs classes; it still requires labeled outcomes. Treating business heuristics (RFM) as ML categories.

Final Answer:

Cluster analysis only