In K-Means clustering, what does ‘K’ represent?
A) The number of iterations
B) The number of clusters
C) The distance metric used
D) The size of the dataset
Show answer
B) The number of clusters
Explanation: The ‘K’ in K-Means represents the number of clusters that the algorithm will partition the data into.
What’s the main goal of the K-Means clustering algorithm?
A) Minimize the sum of squared distances between data points and their assigned cluster centroids ?
B) Maximize the accuracy of classification ?
C) Maximize the distance between clusters ?
D) Minimize the error rate in predictions ?
Show answer
A) To minimize the sum of squared distances between data points and their assigned cluster centroids
Explanation: The goal of K-Means is to minimize the within-cluster variance, which is measured as the sum of squared distances between data points and their respective centroids.
What should you consider if K-Means clustering doesn’t perform well on your dataset?
A) Try a different clustering algorithm ?
B) Change the initialization method ?
C) Scale the features properly ?
D) All of the above ??
Show answer
D) All of the above
Explanation: Improving K-Means results can involve trying different clustering algorithms, changing the initialization method, and properly scaling the features.
Why would you use K-Means clustering in a customer segmentation task?
A) To predict future customer behavior ?
B) To group customers with similar behavior for targeted marketing ?
C) To identify fraudulent transactions ?
D) To reduce dimensionality of the dataset ?
Show answer
B) To group customers with similar behavior for targeted marketing
Explanation: K-Means can help segment customers based on similar behaviors or attributes, allowing for more targeted marketing strategies.
What’s a potential drawback of K-Means clustering?
A) It’s computationally expensive for small datasets ?
B) It struggles with large datasets ?
C) It assumes spherical clusters ?
D) It can’t handle numerical data ?
Show answer
C) It assumes spherical clusters
Explanation: K-Means assumes that clusters are spherical and evenly sized, which might not always be the case in real-world data.
How can you address the issue of different feature scales before applying K-Means clustering?
A) By using feature scaling (e.g., standardization or normalization) ?
B) By increasing the number of iterations ?
C) By decreasing the number of clusters ?
D) By using a different distance metric ?
Show answer
A) By using feature scaling (e.g., standardization or normalization)
Explanation: Scaling features ensures that each feature contributes equally to the distance calculation, which is crucial for the performance of K-Means.
Discover more from Science Comics
Subscribe to get the latest posts sent to your email.
