10 Questions
What does k-means clustering aim to minimize?
Sum of squared deviations
In what kind of cells does k-means clustering result in a partitioning of the data space?
Voronoi cells
Which algorithm for mixtures of Gaussian distributions is similar to k-means clustering?
Expectation-maximization algorithm
What problem is computationally difficult and NP-hard?
k-means clustering
What does the mean optimize in the context of k-means clustering?
Squared errors
What is the relationship between the unsupervised k-means algorithm and the k-nearest neighbor classifier?
The 1-nearest neighbor classifier can be applied to the cluster centers obtained by k-means
What does k-means clustering aim to minimize?
Within-cluster sum of squares (WCSS)
Who first used the term 'k-means'?
James MacQueen
What is another name for the standard algorithm of k-means clustering?
Lloyd's algorithm
What prevents the naive k-means algorithm from converging?
Using a different distance function other than squared Euclidean distance
Study Notes
K-Means Clustering
- K-means clustering aims to minimize the sum of the squared distances between the data points and their assigned cluster centers.
Partitioning of Data Space
- K-means clustering results in a partitioning of the data space into Voronoi cells.
Similar Algorithm
- The Expectation-Maximization (EM) algorithm for mixtures of Gaussian distributions is similar to k-means clustering.
Computational Difficulty
- The problem of finding the optimal number of clusters (k) is computationally difficult and NP-hard.
Mean Optimization
- The mean optimizes the sum of the squared distances between the data points and their assigned cluster centers in the context of k-means clustering.
Relationship with k-Nearest Neighbor
- The unsupervised k-means algorithm is similar to the k-nearest neighbor (k-NN) classifier in the sense that both are based on Euclidean distance.
Origin of Term 'k-Means'
- The term 'k-means' was first used by James MacQueen in 1967.
Standard Algorithm
- Another name for the standard algorithm of k-means clustering is Lloyd's algorithm.
Convergence Issue
- The naive k-means algorithm may not converge due to the existence of local optima, which prevents convergence.
Test your knowledge of the k-means clustering algorithm, a method of vector quantization used to partition observations into clusters based on their nearest mean. Explore how k-means clustering minimizes within-cluster variances and partitions data space into Voronoi cells.
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