The goal of clustering a set of data is to:

Question

BlackTom AI · Accepted Answer

Clustering groups data points so that similar (nearby) points are placed together and dissimilar points fall into different groups.

1. Relevant knowledge points and concepts
- Clustering is an unsupervised learning task: there are no labeled target classes. 
- Key idea is similarity measured by a distance metric (e.g., Euclidean, Manhattan, cosine). 
- Good clusters have high intra-cluster similarity (points inside a cluster are close) and low inter-cluster similarity (clusters are well separated). 
- Common algorithms: k-means (minimizes within-cluster variance), hierarchical clustering (builds nested clusters), DBSCAN (finds dense regions).

2. Complete reasoning process step by step
- Start with a dataset of unlabeled points.  
- Choose a notion of distance d(x,y) to quantify how “near” two points are.  
- Partition the dataset into groups C1, C2, …, Ck such that points in each Ci are more similar to each other than to points in other clusters.  
- Objective functions used by algorithms formalize this: for example, k-means minimizes sum over clusters of sum_{x in Ci} ||x - μi||^2 where μi is the cluster centroid. This enforces proximity inside clusters.

3. Calculations / intermediate steps (conceptual)
- For k-means the quantity minimized is ∑_{i=1..k} ∑_{x∈Ci} ||x − μi||^2 — making within-cluster distances small.
- For DBSCAN, clusters are areas where each point has at least minPts within radius ε — enforcing local nearness.

4. Why the correct answer is correct
- The phrase “divide them into groups of data that are near each other” precisely captures the aim: grouping by proximity/similarity without labels. Clustering’s core objective is forming such groups.

5. Why other options are incorrect
- “Choose the best data from the set” is vague and not clustering—sounds like selection or sampling.  
- “Determine the nearest neighbors of each of the data” describes neighbor search (k-NN search), not forming groups.  
- “Predict the class of data” is classification (supervised learning), which requires labeled examples and assigns class labels rather than discovering groups.

6. Conclusion
This reasoning confirms the correct answer: divide them into groups of data that are near each other.

The goal of clustering a set of data is to:单项选择题

类似问题

Q34. A student writes: kmeans = KMeans(n_clusters=3) df["cluster"] = kmeans.fit(X) What is the issue?

Which of the following statements about clustering in machine learning is true?

Your team experiments with increasing K in K-Means from 3 to 8. What is the expected effect?

In k-means clustering, what is the primary goal of the algorithm? (you can choose more than one)

The elbow plot helps determine the optimal number of clusters by looking for:

What does clustering accomplish?

Which method is best for detecting patterns in unlabeled data?

Which of the descriptions listed below is TRUE?

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单