Practical Data Science using Python - K-Means Clustering Computation

The video tutorial explains the K-means clustering algorithm, focusing on determining the number of clusters (K) and optimizing centroids. It covers the process of initializing random centroids, calculating Euclidean distances, and iteratively refining centroids to minimize the sum of squared distances. A detailed example illustrates these steps, emphasizing the importance of centroid optimization in achieving accurate clustering results.

What is the primary goal of K-Means clustering?

• To find the optimal number of centroids

• To predict future data points

• To classify data into predefined categories

• To determine the best regression line

In K-Means clustering, what does the 'K' represent?

• The number of iterations

• The number of dimensions

• The number of data points

• The number of centroids

What is a centroid in the context of K-Means clustering?

• The center point of a cluster

• A data point with the highest value

• The farthest point from the origin

• A randomly selected data point

How is the Euclidean distance used in K-Means clustering?

• To determine the distance between centroids

• To calculate the average of data points

• To find the maximum distance between clusters

• To assign data points to the nearest centroid

What is the first step in the K-Means clustering process?

• Calculate the mean of each cluster

• Determine the value of K

• Optimize the centroids

• Assign data points to clusters

During the K-Means process, how are new centroids calculated?

• By averaging the coordinates of data points in a cluster

• By selecting random data points

• By using the median of the data points

• By choosing the farthest data point

In the worked example, what is the purpose of calculating squared distances?

• To minimize the cost function

• To find the largest cluster

• To determine the number of clusters

• To predict future data points

What indicates that the K-Means clustering process has converged?

• The centroids continue to move

• The number of clusters changes

• The sum of squared distances increases

• The centroids remain stable

What is the role of the cost function in K-Means clustering?

• To determine the number of iterations

• To minimize the sum of squared distances

• To calculate the average of data points

• To maximize the distance between clusters

How can you verify that the K-Means algorithm has reached an optimal solution?

• By increasing the number of clusters

• By counting the number of data points

• By ensuring the sum of squared distances is minimized

• By checking if the centroids are at the origin