K-Means Quiz

What is k-means clustering?

• K-means clustering is a regression algorithm.

• K-means clustering is a machine learning algorithm used to partition a dataset into groups or clusters based on their similarity.

• K-means clustering is used to classify images.

• K-means clustering is a supervised learning algorithm.

What are the algorithm steps of k-means clustering?

• Assign centroids, initialize data points, recalculate centroids, repeat until convergence

• Assign data points, initialize centroids, recalculate centroids, repeat until convergence

• Recalculate centroids, assign data points, initialize centroids, repeat until convergence

• Initialize centroids, assign data points, recalculate centroids, repeat until convergence

How is the number of clusters chosen in k-means clustering?

• By randomly selecting a number of clusters

• By using techniques such as the elbow method or silhouette analysis.

• By using the average silhouette width

• By choosing the number of clusters based on the maximum within-cluster sum of squares

What are some common methods for evaluating k-means clustering?

• random assignment

• hierarchical clustering

• elbow method, silhouette coefficient, and gap statistic

• dendrogram analysis

What are some applications of k-means clustering?

• Customer segmentation, image compression, document clustering, anomaly detection, and recommendation systems

• Speech recognition, network traffic analysis, and social media analysis

• Text classification, fraud detection, and market segmentation

• Image recognition, sentiment analysis, and time series forecasting

In k-means clustering, what is the role of the centroid?

• The centroid is a point that represents the average of all data points in the cluster.

• The centroid is a random point chosen as the center of the cluster.

• The centroid represents the center of a cluster.

• The centroid is a data point that is closest to all other points in the cluster.

What is the objective function used in k-means clustering?

• Sum of mean distances

• Sum of squared distances

• Sum of squared errors

• Sum of absolute distances

How does k-means handle outliers in the data?

• K-means removes outliers from the data

• K-means does not handle outliers

• K-means assigns outliers to the nearest cluster

• K-means treats outliers as separate clusters

What are the limitations of k-means clustering?

• 1) Does not work well with large datasets. 2) Requires equal variance of variables within each cluster. 3) Cannot handle categorical variables.

• The limitations of k-means clustering include: 1) Requires the number of clusters to be specified in advance. 2) Sensitive to initial placement of cluster centroids. 3) Assumes clusters are spherical and of equal size. 4) May not work well with outliers or noise. 5) May not be suitable for datasets with non-linear or complex structures.

The Learning which is used to find the hidden pattern in the unlabeled data is called _______

• Supervised

• Unsupervised

• Reinforcement

• Semi-supervised