Clustering Flashcards

1
Q
  • Introduce the unsupervised learning problem
  • What is clustering (definition)?
  • What are the difficulties of clustering?
  • Model for clustering
  • What are the 2 Classes of Algorithms for Clustering?
A

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2
Q

Cost Minimization Clustering

  • Approach and assumptions.
  • What are the 3 main objective functions?
A

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3
Q
  • What is a brute force algorithm to sole K-means? What is its problem?
  • Lloyd’s Algorthm, its convergence criteria, its complexity, after how many iterations it stops?
  • kmeans++
A

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4
Q

Linkage-Based Clustering

  • How the algorithm works?
  • What are the 2 parameters that it needs?
  • What is a dendrogram? What it represents?
  • How can we choose the number k of clusters? What is and how Silhouette works?
  • Clustering for Predictions.
A

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