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Questions and Answers
What is the primary objective of K-means clustering?
What is the primary objective of K-means clustering?
- Identifying the number of clusters in a dataset
- Calculating the sum of squares for each data point
- Finding the centroid of each cluster
- Grouping similar data points together (correct)
How are centroids defined in K-means clustering?
How are centroids defined in K-means clustering?
- As the number of clusters in a dataset
- As the sum of squares for each data point
- As the average data point in a cluster
- As the imaginary location representing the center of a cluster (correct)
What does the 'means' in K-means refer to?
What does the 'means' in K-means refer to?
- Grouping data points based on similarities
- Averaging the data to find centroids (correct)
- Calculating the sum of squares for data points
- Finding the centroid of clusters
How does K-means allocate data points to clusters?
How does K-means allocate data points to clusters?
What role does the target number 'k' play in K-means clustering?
What role does the target number 'k' play in K-means clustering?
In K-means clustering, what does each cluster represent?
In K-means clustering, what does each cluster represent?
What is the main objective of K-means clustering?
What is the main objective of K-means clustering?
What does the target number 'k' represent in K-means clustering?
What does the target number 'k' represent in K-means clustering?
How are data points allocated to clusters in K-means clustering?
How are data points allocated to clusters in K-means clustering?
What is a centroid in the context of K-means clustering?
What is a centroid in the context of K-means clustering?
How does K-means handle the allocation of data points to clusters?
How does K-means handle the allocation of data points to clusters?
What is the role of 'means' in the K-means algorithm?
What is the role of 'means' in the K-means algorithm?
Why does K-means look for a fixed number (k) of clusters in a dataset?
Why does K-means look for a fixed number (k) of clusters in a dataset?
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Study Notes
K-Means Clustering
- K-means clustering is a popular unsupervised machine learning algorithm.
- Unsupervised algorithms make inferences from datasets without referring to known or labeled outcomes.
Objective of K-Means
- The objective of K-means is to group similar data points together and discover underlying patterns.
- K-means achieves this objective by looking for a fixed number (k) of clusters in a dataset.
Key Concepts
- A cluster is a collection of data points aggregated together due to certain similarities.
- A centroid is the imaginary or real location representing the center of the cluster.
- The target number k refers to the number of centroids needed in the dataset.
How K-Means Works
- The K-means algorithm identifies k number of centroids.
- Every data point is allocated to the nearest cluster, while keeping the centroids as small as possible.
- The algorithm allocates data points to clusters through reducing the in-cluster sum of squares.
The Meaning of 'Means' in K-Means
- The 'means' in K-means refers to averaging of the data, i.e., finding the centroid.
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