offlineChange (version 0.0.4)

OrderKmeans: Detect Location of Change Points of Independent Data

Description

Detect the location of change points based on minimizing within segment quadratic loss with fixed number of change points.

Usage

OrderKmeans(x, K = 4, num_init = 10)

Arguments

x

The data to find change points with dimension N x D, must be matrix

K

The number of change points.

num_init

The number of repetition times, in order to avoid local minimum. Default is 10. Must be integer.

Value

wgss_sum

total within segment sum of squared distances to the segment mean (wgss) of all segments.

num_each

number of observations of each segment

wgss

total wgss of each segment.

change_point

location of optimal change points.

Details

The K change points form K+1 segments (1 2 ... change_point(1)) ... (change_point(K) ... N).

References

J. Ding, Y. Xiang, L. Shen, and V. Tarokh, Multiple Change Point Analysis: Fast Implementation and Strong Consistency. IEEE Transactions on Signal Processing, vol. 65, no. 17, pp. 4495-4510, 2017.

Examples

# NOT RUN {
a<-matrix(rnorm(40,mean=-1,sd=1),nrow=20,ncol=2)
b<-matrix(rnorm(120,mean=0,sd=1),nrow=60,ncol=2)
c<-matrix(rnorm(40,mean=1,sd=1),nrow=20,ncol=2)
x<-rbind(a,b,c)
OrderKmeans(x,K=3)
OrderKmeans(x,K=3,num_init=8)
# }