Understanding K-means Clustering Basics

Which of the following is the first step in the K-means clustering algorithm?

• Assign each data point to the nearest cluster centre

• Calculate the silhouette score

What does the 'K' in K-means represent?

• The number of features in the dataset

• The number of clusters to form

• The number of iterations

• The number of data points

Which distance metric is most commonly used in the standard K-means algorithm?

• Manhattan distance

• Cosine similarity

• Euclidean distance

• Hamming distance

After assigning each data point to the nearest cluster centre, what is the next step in the K-means algorithm?

• Remove outliers from the dataset

• Recalculate the cluster centres (means)

• Calculate the silhouette score

• The elbow method

• The silhouette method

• Random selection

• Gap statistic

Which of the following is a real-world application of K-means clustering?

• Predicting stock prices

• Grouping customers based on purchasing behaviour

• Sorting emails by date

• Calculating the mean temperature

What is the main objective of the K-means algorithm?

• Maximise the distance between clusters

• Minimise the sum of squared distances between data points and their cluster centre

• Maximise the number of clusters

• Minimise the number of features

Which of the following is a limitation of the K-means algorithm?

• It can only be used for supervised learning

• It works only with categorical data

• It always finds the global optimum

Which evaluation method measures how similar an object is to its own cluster compared to other clusters?

• Elbow method

• Silhouette score

• Gap statistic

• Davies-Bouldin index