# norm.sim.ksc: K-means algorithm based on cosine distance In akmeans: Adaptive Kmeans algorithm based on threshold

## Description

On the assumption that the two samples are already normalized to have L2 norm as 1, cosine distance is defined as 1 - inner product of the two samples.

## Usage

 `1` ```norm.sim.ksc(A, k, init.cen = NULL, init.mem = NULL, iter.max = 100) ```

## Arguments

 `A` n by p matrix, each row is a sample `k` the number of clusters `init.cen` initial cluster centers `init.mem` initial cluster member assignment `iter.max` the maximum number of iteration

## Value

A list will be returned with components : cluster: A vector of integers indicating the cluster to which each point is allocated. centers: A matrix of cluster centres size: The number of points in each cluster

Jungsuk Kwac

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```############### ## test code ## 4 classes: a1,a2,a3,a4 ## for each class, 20 samples ############### n = 20; p = 32 a1 = 10*sin(0.1*(1:p)) a2 = 10*cos(0.1*(1:p))+10 a3 = c(1:(p/2),(p/2):1) a4 = c((p/2):1,1:(p/2)) A = c() for (i in 1:n){ A = rbind(A,a1+rnorm(p),a2+rnorm(p),a3+rnorm(p),a4+rnorm(p)) } res = norm.sim.ksc(quick.norm(A,1),4) ```

### Example output

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akmeans documentation built on May 2, 2019, 2:40 a.m.