## Introduction

yakmoR is a simple wrapper for the K-Means C++ library (yakmo) developed by Naoki Yoshinaga.

yakmoR implements orthogonal K-Means. It can work in several rounds. In the first round, a normal K-Means is applied to the data. In each subsequent round, the next clustering is done on a subspace orthogonal to the centroids of the last clustering. This way one produces different views on the data. To speed up the whole procedure, Greg Hamerlys faster K-Means is utilized. Initilization can be done either classically (uniformly random) or by using the K-Means++ scheme.

## Example

library(yakmoR)

data(iris)
irisM = as.matrix(iris[sample(nrow(iris)), -5]) # convert to matrix, also remove class-information
dat = irisM[1:100, ] # take first 100 data points for clustering
resObj = yakmoR::orthoKMeansTrain (x = dat, k = 3,  rounds = 4)
centers2 = resObj\$centers[[2]] # centers of 2nd round

dat = as.matrix( irisM[101:nrow(irisM), -5]) # take rest of data for prediction
results = yakmoR::orthoKMeansPredict (x = dat, obj = resObj)