model 5 — K-means
1 definition
- randomly initialize K cluster centroids μ 1 , μ 2 , ⋯ \mu_1, \mu_2, \cdots μ1,μ2,⋯
- repeat:
- assign each point to its closest centroid μ \mu μ
- recompute the centroids(average of the closest point)
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2 optimazation objective
- c ( i ) c^{(i)} c(i) = index of cluster to which example x ( i ) x^{(i)} x(i) is currently assigned
- μ k \mu_k μk = cluster centroid k
- μ c ( i ) \mu_{c^{(i)}} μc(i) = cluster centroid of cluster to which example x ( i ) x^{(i)} x(i) has been assigned
J = 1 m ∑ i = 1 m ∥ x ( i ) − μ c ( i ) ∥ J = \frac{1}{m} \sum_{i=1}^m \| x^{(i)} - \mu_{c^{(i)}} \| J=m1i=1∑m∥x(i)−μc(i)∥文章来源地址https://www.toymoban.com/news/detail-813136.html
3 randomly initialization
for i = 1 to n(usually 50 to 1000)
randomly initialize K-means
run K-means
compute cost function
pick set of clusters that give the lowest cost
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