"Statistical learning methods" Chapter 14 Clustering KMeans - Code World

"Statistical learning methods" Chapter 14 Clustering KMeans

Others 2019-06-27 07:39:00 views: null

k- means clustering

k n samples assigned to different classes or clusters, each sample from its center to a minimum belongs.

Each sample can only belong to a class, all k- means clustering is hard clustering .

model

k < n
$G_{i} \cap G_{j} = \varnothing, \bigcup_{i=1}^{k}G_{i} = X$

Tactics

Distance: Euclidean distance
Loss function: distance from the center of the sample belongs based on the total retention
NP-hard problem

algorithm

The objective function minimization

Initialization, randomly selected $k$ samples do center
Clustering the samples, the sample is calculated from the cluster center, each sample to its nearest center is assigned a class
The new class of computing centers. Calculate the mean of the sample clustering results, as a new class center
If the iteration converge or meet the conditions to stop the output. Otherwise, let $t = t + 1$ returns 2

Source: github.com/iOSDevLog/s...

Reproduced in: https: //juejin.im/post/5cfe7baef265da1b6a348a19

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