Review K-means results

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Dmitriy (Dima) Gorenshteyn

Lead Data Scientist, Memorial Sloan Kettering Cancer Center

Three clustering results

oes_clusters

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Comparing the two clustering methods

Hierarchical Clustering k-means
Distance Used: virtually any euclidean only
Results Stable: Yes No
Evaluating # of Clusters: dendrogram, silhouette, elbow silhouette, elbow
Computation Complexity: Relatively Higher Relatively Lower

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What have you learned?

  • Chapter 1:
    • What is distance
    • Why is scale important
  • Chapter 2:
    • How linkage works
    • How the dendrogram is formed
    • How to analyze your clusters
  • Chapter 3:
    • How k-means works
    • How to estimate k
    • How to analyze how well an observation fits in a cluster
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A lot more to learn

  • k-mediods
  • DBSCAN
  • Optics
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Congratulations!

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