Which method is best for detecting patterns in unlabeled data?

Question

BlackTom AI · Accepted Answer

When given unlabeled data, the goal is to uncover inherent structure or groupings without predefined class labels. 
Option 1: Logistic Regression. This method is fundamentally supervised and used for classification with labeled outcomes, so it isn’t suitable for discovering patterns in unlabeled data. 
Option 2: Linear Regression. Similar to logistic regression, this approach is supervised and aims to predict a continuous target based on labeled data, not discover clusters in unlabeled inputs. 
Option 3: Clustering. This is the core unsupervised learning technique designed to detect natural groupings in data without labeled responses, making it the best fit for pattern detection in unlabeled datasets. 
Option 4: Decision Trees. While powerful for supervised tasks like classification and regression, they rely on labeled targets to split data and learn predictive rules, so they aren’t the primary tool for exploring unlabeled structure. 
In summary, clustering uniquely emphasizes identifying patterns and groupings without prior labels, whereas the other methods assume labeled outcomes and supervised learning objectives.

Which method is best for detecting patterns in unlabeled data?

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