Which of the following is an example of unsupervised learning technique?

Question

BlackTom AI · Accepted Answer

Co-occurrence grouping is an example of unsupervised learning because it discovers structure in unlabeled data.

1. Relevant knowledge points and concepts
- Supervised learning: algorithms learn a mapping from inputs to known labels (examples: classification trees, regression). Training data include target labels.
- Unsupervised learning: algorithms discover patterns, clusters, or associations in data without labeled outputs (examples: clustering, association/co-occurrence mining, dimensionality reduction).
- Co-occurrence grouping refers to methods that find which items/events tend to appear together (association rules, market-basket analysis, co-occurrence matrices), which is inherently exploratory and label-free.

2. Step-by-step reasoning
- Examine each option and classify it as supervised or unsupervised:
  a) Co-occurrence grouping — finds associations among items without target labels → unsupervised.
  b) Classification tree — constructs a model that predicts discrete class labels from features → supervised.
  c) Causal modeling — aims to infer cause–effect relationships; typically requires labeled outcomes or interventions and is not a standard unsupervised discovery method (it’s model-driven and often treated separately from basic unsupervised techniques).
  d) Regression analysis — estimates continuous target values from predictors → supervised.

3. Intermediate steps (mapping options to categories)
- Option a ⇒ unsupervised: no labels needed, looks for frequent itemsets/co-occurrence patterns.
- Option b ⇒ supervised: needs labeled classes to train decision tree.
- Option c ⇒ not a canonical unsupervised technique: requires specification of causal variables/outcomes; often uses experimental/observational labels.
- Option d ⇒ supervised: fits a function to labeled numerical targets.

4. Why the correct answer is correct
- Co-occurrence grouping uncovers relationships and groupings purely from input data frequencies and co-occurrence statistics. Because it does not rely on pre-assigned target labels, it matches the definition of unsupervised learning (e.g., association rule mining, clustering).

5. Why the other options are incorrect
- Classification tree: requires labeled classes and is a supervised classification technique.
- Causal modeling: focuses on causal inference rather than pure unlabeled pattern discovery and typically involves modeling directed relationships with outcome variables.
- Regression analysis: requires numeric target variables to learn predictions and is a supervised method.

6. Conclusion
The correct answer is Co-occurrence grouping. This matches the provided answer: "Co-occurrence grouping."

Which of the following is an example of unsupervised learning technique?单项选择题

类似问题

What kind of machine learning setting is usually used for grouping?

Of the 3 main AI/machine learning techniques, the one that identifies clusters within a dataset without any pre-defined or labelled categories is an example of __________ learning.

Which of the following is used almost exclusively for pattern detection on unlabeled data where the system is not told what to look for specifically but to simply discover patterns in the data?

What kind of machine learning setting is usually used for clustering?

What is clustering?

Question 1

Which scenario is MOST clearly unsupervised?

What is the main goal of unsupervised learning?

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