Which algorithm groups customers by demographics and buying patterns?
Short answer
B K-means
Question
Key Takeaways
This question tests the ability to distinguish between supervised learning (classification) and unsupervised learning (clustering), with the keyword 'find groups' being the critical indicator of a clustering task.
The question tests knowledge of unsupervised clustering algorithms, specifically K-means, for customer segmentation based on demographics and buying patterns. Community consensus strongly supports K-means as the correct choice for grouping unlabeled data into natural clusters.
Common mistake: Many candidates incorrectly choose K-nearest neighbors (k-NN) because it also contains the letter 'K' and involves grouping concepts, but k-NN is a supervised classification algorithm that requires labeled training data, not an unsupervised clustering method.
Expert Analysis
Understanding the Problem
The key phrase in this question is "find groups" for customers based on their characteristics. This immediately signals an unsupervised learning task, specifically clustering, where the goal is to discover natural groupings in data without pre-existing labels.
Why K-means is Correct
K-means is a classic clustering algorithm that partitions data into K distinct groups (clusters) based on feature similarity. In this scenario:
- Customer demographics (age, income, location) and buying patterns (purchase frequency, average spend, product categories) serve as the features
- K-means automatically discovers natural customer segments by minimizing the distance between data points within each cluster
- The result is distinct customer groups that can be used for targeted marketing and personalized offerings
Why Other Options Are Wrong
A. K-nearest neighbors (k-NN): This is a supervised classification algorithm that requires labeled training data. It classifies new data points based on the majority class of their K nearest neighbors. Since the company wants to "find groups" (discover unknown segments), not classify customers into known categories, k-NN is inappropriate.
C. Decision tree: This is also a supervised learning algorithm used for classification or regression. It requires labeled data to learn decision rules. Decision trees don't discover natural groupings; they classify based on learned patterns from labeled examples.
D. Support vector machine: SVM is another supervised classification algorithm that finds optimal boundaries between predefined classes. Like decision trees, it requires labeled training data and cannot discover unknown customer segments.
Community Insights
As noted by community members, the distinction is clear: "K-means - clustering - groups" while the other algorithms are for classification tasks. The keyword "groups" is the giveaway that this is a clustering problem requiring an unsupervised approach.
Official Reference
- https://learn.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/k-means-clustering
- https://scikit-learn.org/stable/modules/clustering.html#k-means
These links open the official vendor documentation in a new tab.
Exam Strategy
When you see keywords like 'find groups,' 'segment,' or 'discover patterns' without mention of labels or categories, immediately think 'unsupervised learning' and 'clustering.' Eliminate all supervised algorithms (k-NN, decision trees, SVM, logistic regression) from consideration.
Community Votes
80% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Discussion 6 contributions
Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.
From public community threads 6
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kopper2019 👍 1 Answered ALet's break down why: Why K-means is correct: The company wants to find "groups" of customers → This indicates a clustering task K-means is specifically designed for grouping/clustering similar data points It works well with multiple features (demographics AND buying patterns) K-means can automatically discover natural groupings in customer data It's commonly used for customer segmentation in business applications Why other options are incorrect: A (K-nearest neighbors): This is for classification when you already have labeled data, not for discovering groups
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chdaphne 👍 1 Answered BK-means is a clustering algorithm widely used for customer segmentation. It groups customers based on similarities in their demographics and buying patterns, creating distinct clusters that can be analyzed for targeted marketing strategies or personalized product offerings. This algorithm is efficient, interpretable, and works well with large datasets, making it suitable for e-commerce applications.
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Jessiii 👍 1 Answered BK-means is a clustering algorithm that groups data points into clusters based on their similarities. It is particularly well-suited for unsupervised learning tasks where the goal is to identify natural groupings within the data, such as segmenting customers based on demographics and buying patterns.
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AzureDP900 👍 1 Answered BAnswer: B. K-means The company should use K-means to group customers based on demographics and buying patterns. K-means is an unsupervised clustering algorithm that effectively partitions data into natural groups, making it ideal for discovering customer segments without prior labeling.
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kopper2019 👍 1A. K-nearest neighbors (k-NN) - classification B. K-means - clustering - groups, so B
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chris_spencer 👍 1 Answered BK-means is a clustering algorithm
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