Curve: Curve Data Set

In clues: Clustering Method Based on Local

Description

A toy example used to illustrate curve clustering.

Usage

data(Curve)

Format

A list contains a 300 by 10 data matrix (curve) and a 300 by 1 cluster membership vector (curve.mem). There are 3 clusters, each containing 100 data points, respectively, in a 10-dimensional space.

Details

cluster one is generated from the model:

yik = sin(2 * PI * xk) + eik, xk ~ N(0, 1), eik ~ N(0, 0.1), i = 1, …, 100, k = 1, …, 10.

cluster two is generated from the model:

yik = cos(2 * PI * xk) + eik, xk ~ N(0, 1), eik ~ N(0, 0.1), i = 1, ..., 100, k = 1, ..., 10.

cluster three is generated from the model:

yik = eik, eik ~ N(0, 1), i = 1, ..., 100, k = 1, ..., 10.

Examples

    data(Curve)
 
    # data matrix
    curve <- Curve$curve
 
    # 'true' cluster membership
    curve.mem <- Curve$curve.mem
    # 'true' number of clusters
    nClust <- length(unique(curve.mem))
 
    # plot average trajectories
    plotAvgCurves(curve, curve.mem)

clues documentation built on Dec. 4, 2019, 1:09 a.m.