K-Means Clustering Quiz
10 Questions
6 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

What does k-means clustering aim to minimize?

  • Sum of squared deviations (correct)
  • Variance
  • Mean absolute error
  • Regular Euclidean distances
  • In what kind of cells does k-means clustering result in a partitioning of the data space?

  • Triangular cells
  • Voronoi cells (correct)
  • Rectangular cells
  • Hexagonal cells
  • Which algorithm for mixtures of Gaussian distributions is similar to k-means clustering?

  • Mean-shift algorithm
  • Expectation-maximization algorithm (correct)
  • Agglomerative clustering algorithm
  • DBSCAN algorithm
  • What problem is computationally difficult and NP-hard?

    <p>k-means clustering</p> Signup and view all the answers

    What does the mean optimize in the context of k-means clustering?

    <p>Squared errors</p> Signup and view all the answers

    What is the relationship between the unsupervised k-means algorithm and the k-nearest neighbor classifier?

    <p>The 1-nearest neighbor classifier can be applied to the cluster centers obtained by k-means</p> Signup and view all the answers

    What does k-means clustering aim to minimize?

    <p>Within-cluster sum of squares (WCSS)</p> Signup and view all the answers

    Who first used the term 'k-means'?

    <p>James MacQueen</p> Signup and view all the answers

    What is another name for the standard algorithm of k-means clustering?

    <p>Lloyd's algorithm</p> Signup and view all the answers

    What prevents the naive k-means algorithm from converging?

    <p>Using a different distance function other than squared Euclidean distance</p> Signup and view all the answers

    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.

    Studying That Suits You

    Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

    Quiz Team

    Description

    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.

    More Like This

    K-Means-Clustering-Quiz
    27 questions
    K Means Clustering Quiz
    3 questions

    K Means Clustering Quiz

    ProtectiveJudgment avatar
    ProtectiveJudgment
    K-Means Clustering Algorithm
    58 questions
    Use Quizgecko on...
    Browser
    Browser