The post is about MCQs cluster Analysis. There are 20 multiple-choice questions from clustering, covering topics such as k-means, k-median, k-means++, cosine similarity, k-medoid, Manhattan Distance, etc. Let us start with the MCQs Cluster Analysis Quiz.
Online Multiple-Choice Questions about Cluster Analysis
Online MCQs Cluster Analysis
- Which of the following statements is true?
- What are some common considerations and requirements for cluster analysis?
- Which of the following statements is true?
- Which of the following statements is true?
- Which of the following statements about the K-means algorithm are correct?
- Which of the following statements, if any, is FALSE?
- In the figure below, Map the figure to the type of link it illustrates.
- In the figure below, Map the figure to the type of link it illustrates.
- In the figure below, Map the figure to the type of link it illustrates.
- Considering the k-median algorithm, if points $(-1, 3), (-3, 1),$ and $(-2, -1)$ are the only points that are assigned to the first cluster now, what is the new centroid for this cluster?
- Which of the following statements about the K-means algorithm are correct?
- Given the two-dimensional points (0, 3) and (4, 0), what is the Manhattan distance between those two points?
- Given three vectors $A, B$, and $C$, suppose the cosine similarity between $A$ and $B$ is $cos(A, B) = 1.0$, and the similarity between $A$ and $C$ is $cos(A, C) = -1.0$. Can we determine the cosine similarity between $B$ and $C$?
- Is K-means guaranteed to find K clusters that lead to the global minimum of the SSE?
- The k-means++ algorithm is designed to better initialize K-means, which will take the farthest point from the currently selected centroids. Suppose $k = 2$ and we have chosen the first centroid as $(0, 0)$. Among the following points (these are all the remaining points), which one should we take for the second centroid?
- Which of the following statements is true?
- Suppose $X$ is a random variable with $P(X = -1) = 0.5$ and $P(X = 1) = 0.5$. In addition, we have another random variable $Y=X * X$. What is the covariance between $X$ and $Y$?
- For k-means, will different initializations always lead to different clustering results?
- In the k-medoids algorithm, after computing the new center for each cluster, is the center always guaranteed to be one of the data points in that cluster?
- In the k-median algorithm, after computing the new center for each cluster, is the center always guaranteed to be one of the data points in that cluster?
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