Practical Data Science using Python - K-Means Clustering Optimization

The video tutorial explains K-means clustering, emphasizing its nondeterministic nature due to random initial centroids. It highlights the importance of data standardization to handle outliers and scale differences. Two methods for optimizing the number of clusters, K, are discussed: the elbow method, which uses the sum of squared errors, and the silhouette method, which evaluates cluster quality using silhouette scores. The tutorial concludes with a practical example using Python for customer segmentation.

What is a key characteristic of the K-Means algorithm?

• It is deterministic.

• It does not require data standardization.

• It always produces the same clusters.

• It is nondeterministic.

Why is data standardization important before applying K-Means?

• To avoid using the Elbow Method.

• To increase the number of clusters.

• To ensure all features are on the same scale.

• To make the algorithm deterministic.

What does the Elbow Method help determine in K-Means clustering?

• The initial centroids.

• The data standardization technique.

• The optimal number of clusters.

• The distance metric to use.

In the Elbow Method, what does the 'elbow' point represent?

• The point where the silhouette score is highest.

• The point where the number of clusters is maximum.

• The point where SSE stops decreasing significantly.

• The point where SSE starts to increase.

What is the silhouette score used for in clustering?

• To calculate the SSE.

• To evaluate the quality of clustering.

• To standardize the data.

• To determine the initial centroids.

How is the silhouette coefficient calculated?

• By subtracting AI from BI and dividing by the maximum of AI and BI.

• By adding AI and BI.

• By multiplying AI and BI.

• By dividing AI by BI.

What is the next step after determining the optimal number of clusters using the Elbow and Silhouette methods?

• Run K-Means with the optimized value of K.

• Run K-Means with the initial arbitrary value of K.

• Increase the number of features.

• Change the distance metric.