K-Means Clustering Quiz

What is a key requirement before starting a K-Means clustering analysis?

• Choosing the data points

• Defining the number of clusters

• Deciding the size of clusters

• Selecting the color of clusters

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

• Repeating the process

• Defining the number of clusters

• Calculating cluster centers

• Assigning points to clusters

How are initial cluster centers set in K-Means clustering?

• By averaging all data points

• Based on the largest data point

• Randomly

• Using the smallest data point

What happens if the cluster distribution does not change in an iteration?

• The data is re-evaluated

• The process is repeated

• New clusters are added

• The process is stopped

Why is it necessary to repeat the K-Means clustering process multiple times?

• To reduce the number of data points

• To increase the number of clusters

• To find the smallest sum of distances

• To ensure the data is accurate

What method is used to determine the optimal number of clusters in K-Means?

• The knee method

• The foot method

• The elbow method

• The shoulder method

What indicates the optimal number of clusters using the elbow method?

• A decrease in data points

• No change in summed distances

• A small gap between cluster numbers

• A large gap between cluster numbers

What tool is demonstrated for performing K-Means clustering online?

• Analysis Hub

• Data Tab

• Cluster Calc

• Cluster Pro

What can be adjusted in Data Tab to get the right number of clusters?

• The size of data points

• The type of data

• The color of clusters

• The number of clusters

What is shown in the plot after performing K-Means clustering in Data Tab?

• The distribution of clusters

• The size of each cluster

• The number of data points

• The color of each cluster