Here are 25 interview questions along with their answers related to K-means clustering
Answer: K-means clustering is an unsupervised machine learning algorithm used to partition a dataset into K clusters based on similarity.
2. How does K-means clustering work?
Answer: K-means clustering works by iteratively assigning data points to the nearest cluster centroid and updating the centroids based on the mean of the data points assigned to each cluster.
3. What is the objective function in K-means clustering?
Answer: The objective function in K-means clustering is to minimize the sum of squared distances between each data point and its assigned cluster centroid.
4. What are the key parameters in K-means clustering?
Answer: The key parameters in K-means clustering are the number of clusters (K) and the initial cluster centroids.
5. How do you choose the number of clusters (K) in K-means clustering?
Answer: The number of clusters (K) in K-means clustering is often chosen based on domain knowledge, the Elbow method, or the Silhouette method.
6. What is the Elbow method in K-means clustering?
Answer: The Elbow method is a technique used to determine the optimal number of clusters by plotting the within-cluster sum of squares against the number of clusters and identifying the "elbow" point where the rate of decrease slows down.
7. What is the Silhouette method in K-means clustering?
Answer: The Silhouette method is a technique used to evaluate the quality of clustering by calculating the silhouette score, which measures how similar a data point is to its own cluster compared to other clusters.
8. What are the assumptions of K-means clustering?
Answer: The main assumptions of K-means clustering are that clusters are spherical and have equal variance, and data points within a cluster are independent and identically distributed.
9. How does K-means handle categorical variables?
Answer: K-means clustering is primarily designed for numerical data, so categorical variables may need to be preprocessed or transformed into numerical representations before applying the algorithm.
10. What are the limitations of K-means clustering?
Answer: Some limitations of K-means clustering include sensitivity to initial centroids, the need to specify the number of clusters in advance, and the assumption of spherical clusters.
11. How can you improve the performance of K-means clustering?
Answer: To improve the performance of K-means clustering, you can try different initialization methods, perform feature scaling, and use dimensionality reduction techniques.
12. What are the differences between K-means and hierarchical clustering?
Answer: K-means clustering partitions the data into a predefined number of clusters, while hierarchical clustering builds a hierarchy of clusters by recursively merging or splitting clusters based on proximity.
13. How do you handle missing values in K-means clustering?
Answer: Missing values can be handled by imputation techniques such as mean, median, or mode imputation, or by excluding incomplete observations from the analysis.
14. Can K-means clustering be used for outlier detection?
Answer: While K-means clustering is not designed for outlier detection, it can indirectly identify outliers as data points that do not fit well into any cluster.
15. What is the impact of outliers on K-means clustering?
Answer: Outliers can significantly affect the centroids and cluster assignments in K-means clustering, leading to suboptimal results and skewed clusters.
16. How do you evaluate the quality of clustering in K-means?
Answer: The quality of clustering in K-means can be evaluated using metrics such as the silhouette score, Davies–Bouldin index, and visual inspection of cluster centroids and boundaries.
17. Can K-means clustering be used for text clustering?
Answer: Yes, K-means clustering can be used for text clustering by representing text data using vectorization techniques such as TF-IDF or word embeddings.
18. What are the computational complexities of K-means clustering?
Answer: The computational complexity of K-means clustering is O(n * k * d), where n is the number of data points, k is the number of clusters, and d is the dimensionality of the data.
19. What is the role of centroids in K-means clustering?
Answer: Centroids represent the centers of clusters in K-means clustering and are updated iteratively to minimize the within-cluster sum of squares.
20. How does the choice of initial centroids affect K-means clustering?
Answer: The choice of initial centroids can significantly impact the final clustering results in K-means, as it may lead to different local optima. Random initialization or smart initialization methods like K-means++ can help mitigate this issue.
21. Can K-means clustering handle non-numeric data?
Answer: K-means clustering is primarily designed for numeric data, but categorical variables can be encoded or transformed into numerical representations before applying the algorithm.
22. What are some real-world applications of K-means clustering?
Answer: Real-world applications of K-means clustering include customer segmentation, image compression, anomaly detection, and document clustering.
23. How do you interpret the cluster centroids in K-means clustering?
Answer: Cluster centroids represent the average values of data points assigned to each cluster and can be interpreted as representative characteristics or prototypes of the cluster.
24. Can K-means clustering handle high-dimensional data?
Answer: K-means clustering can handle high-dimensional data, but the curse of dimensionality may affect its performance, so dimensionality reduction techniques like PCA or t-SNE may be applied beforehand.
25. What are some common distance metrics used in K-means clustering?
Answer: Common distance metrics used in K-means clustering include Euclidean distance, Manhattan distance, and cosine similarity.
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