K-Means Clustering: Machine Learning Interview Prep 12

K-means clustering is like sorting candies into different jars by their colors. It groups similar data points together based on their characteristics. The algorithm aims to minimize the differences within each group and maximize the dissimilarities between groups. Think of it as organizing a messy room by putting similar items into separate boxes to make it easier to find things later.

Photo: Balanced Rock, Big Bend National Park, TX, USA Credit: Tasnim and Kawsar

Let’s check your basic knowledge of K-Means Clustering. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!

Question 1: Select the correct statements about the K-means algorithm. (Select two)
(A) K-means is an unsupervised learning algorithm
(B) K-means is a clustering algorithm
(C) K-means is a supervised learning algorithm
(D) K-means is a classification algorithm

Question 2: Select the correct statement about the applications of K-means clustering?
(A) In marketing, K-Means can help segment customers into different groups based on their behavior, preferences, or purchase patterns. (Customer segmentation)
(B) K-Means can be employed for image compression by clustering similar colors together and reducing the color palette without significant loss of visual quality. (Image compression)
(C) By considering outliers as anomalies, K-Means can help identify unusual or suspicious data points. (Anomaly detection)
(D) All of the above.

Question 3: K stands for the number of clusters in K-Means. Which methods can be used to find the optimal value of K in K-means clustering? (Select two)
(A) Elbow method
(B) Silhouette method
(C) UMAP
(D) DBSCAN

Question 4: One of the methods to find the optimal value of K in K-means clustering is the Silhouette method. Select the correct statement about the Silhouette method.
(A) The silhouette score measures the compactness and separation of clusters.
(B) The silhouette score ranges from -1 to 1, where higher values indicate better-defined clusters.
(C) For different values of k, choose the k-value that maximizes the silhouette score.
(D) All of the above

Question 5: One of the methods to find the optimal value of K in K-means clustering is the Elbow method. Select the correct statement about the Elbow method.
(A) The Elbow method plots the within-cluster sum of squares (WCSS) against the number of clusters (k).
(B) WCSS represents the sum of squared distances between each data point and its centroid within a cluster.
(C) The elbow method suggests choosing the value of k at the “elbow” or inflection point of the curve, where additional clusters start to provide diminishing improvements in reducing WCSS.
(D) All of the above

Question 6: What are the advantages of K-Means clustering? (Select two)
(A) K-Means scales well to large datasets and is computationally efficient, making it suitable for many applications.
(B) K-Means scales well to small datasets and is computationally efficient, making it suitable for many applications.
(C) The resulting clusters in K-Means are easy to interpret since each data point belongs to a specific cluster. It provides meaningful insights into the structure of the data.
(D) The resulting clusters in K-Means are easy to interpret since each data point belongs to multiple clusters. It provides meaningful insights into the structure of the data.

Question 7: What are the advantages of K-Means clustering? (Select two)
(A) K-Means can handle large datasets efficiently. It has a linear time complexity with respect to the number of data points and clusters.
(B) K-Means can handle large datasets efficiently. It has a non-linear time complexity with respect to the number of data points and clusters.
(C) K-Means is parallelizable, meaning it can be distributed across multiple processors or machines, making it faster for larger datasets.
(D) K-Means is not parallelizable, meaning it can’t be distributed across multiple processors or machines, making it slower for larger datasets.

Question 8: What are the drawbacks of K-Means clustering?
(A) Sensitive to initial centroids; requires a predefined number of clusters; struggles with clusters of different shapes, densities, or sizes; affected by outliers; and may converge to local optima.
(B) Insensitive to initial centroids; requires a predefined number of clusters; struggles with clusters of different shapes, densities, or sizes; affected by outliers; and may converge to local optima.
(C) Sensitive to initial centroids; doesn’t require a predefined number of clusters; struggles with clusters of different shapes, densities, or sizes; not affected by outliers; and may converge to local optima.
(D) Insensitive to initial centroids; doesn’t require a predefined number of clusters; struggles with clusters of different shapes, densities, or sizes; affected by outliers; and may converge to global optima.

Question 9: Which statement is correct about the drawbacks of K-Means clustering?
(A) K-Means is sensitive to the initial placement of centroids. Depending on the initial centroids and data distribution, K-Means may converge to a local optimum rather than the globally optimal solution.
(B) You need to specify the number of clusters (K) in advance. Determining the optimal number of clusters can be challenging and may require domain knowledge or additional techniques.
(C) Outliers can significantly impact the clustering result. K-Means tends to assign outliers to the nearest cluster, even if they don’t belong to any specific cluster.
(D) All of the above.

Question 10: What are the advantages of DBSCAN over K-Means clustering?
(A) DBSCAN is a better clustering algorithm for non-spherical clusters, outliers, and an unknown number of clusters.
(B) DBSCAN does not assume that the clusters are spherical.
(C) DBSCAN does not require the number of clusters to be specified in advance.
(D) All of the above.

The solutions will be published in the next Confusion Matrix: Machine Learning Interview Prep 13.

Confusion Matrix: Machine Learning Interview Prep 13

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The solution of K-Nearest Neighbors (KNN): Machine Learning Interview Prep 11 — 1(D), 2(D), 3(D), 4(A, B), 5(D), 6(D), 7(C), 8(C, D), 9(A, C), 10(A, B).

K-Nearest Neighbors (KNN): Machine Learning Interview Prep 11

Reference:
[1] How to Ace The K-Means Algorithm Interview Questions
[2] Data Scientists’ Interview Guide: k-Means
[3] 2 — ML Algo Interview Series: K-Means
[4] StatQuest: K-means clustering
[5] The Ultimate Guide to K-Means Clustering: Definition, Methods and Applications

Principal Component Analysis (PCA) Part 1: Machine Learning Interview Prep 14

Principal Component Analysis (PCA) Part 2: Machine Learning Interview Prep 15