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Machine Learning in Action by Peter Harrington

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Chapter 10. Grouping unlabeled items using k-means clustering

 

This chapter covers
  • The k-means clustering algorithm
  • Cluster postprocessing
  • Bisecting k-means
  • Clustering geographic points

 

The 2000 and 2004 presidential elections in the United States were close—very close. The largest percentage of the popular vote that any candidate received was 50.7% and the lowest was 47.9%. If a percentage of the voters were to have switched sides, the outcome of the elections would have been different. There are small groups of voters who, when properly appealed to, will switch sides. These groups may not be huge, but with such close races, they may be big enough to change the outcome of the election.[1] How do you find these groups of people, and how ...

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