ClusKur {SLBDD}

Cluster Identification Procedure using Projections on Directions of Extreme Kurtosis Coefficient

Description

Identification of groups using projections of a vector of features of each time series in directions of extreme kurtosis coefficient.

Usage

ClusKur(x)

Arguments

x

p by k data matrix: p features or variables for each time series and k time series in columns.

Value

A list containing:

• lbl - Cluster labels (possible outliers get negative labels).

• ncl - Number of clusters.

Examples

data(Stockindexes99world)
S <- Stockindexes99world[,-1]
v1 <- apply(S,2, mean)
v2 <- apply(S,2, sd)
M <- rbind(v1,v2)
out <- ClusKur(M)