| LloydShapes {Anthropometry} | R Documentation |
Lloyd k-means for 3D shapes
Description
The basic foundation of k-means is that the sample mean is the value that minimizes the Euclidean distance from each point, to the centroid of the cluster to which it belongs. Two fundamental concepts of the statistical shape analysis are the Procrustes mean and the Procrustes distance. Therefore, by integrating the Procrustes mean and the Procrustes distance we can use k-means in the shape analysis context.
The k-means method has been proposed by several scientists in different forms. In computer science and pattern recognition the k-means algorithm is often termed the Lloyd algorithm (see Lloyd (1982)).
This function allows us to use the Lloyd version of k-means adapted to deal with 3D shapes. Note that in the generic name of the k-means algorithm, k refers to the number of clusters to search for. To be more specific in the R code, k is referred to as numClust, see next section arguments.
Usage
LloydShapes(array3D,numClust,algSteps=10,niter=10,stopCr=0.0001,simul,verbose)
Arguments
array3D |
Array with the 3D landmarks of the sample objects. Each row corresponds to an observation, and each column corresponds to a dimension (x,y,z). |
numClust |
Number of clusters. |
algSteps |
Number of steps of the algorithm per initialization. Default value is 10. |
niter |
Number of random initializations (iterations). Default value is 10. |
stopCr |
Relative stopping criteria. Default value is 0.0001. |
simul |
Logical value. If TRUE, this function is used for a simulation study. |
verbose |
A logical specifying whether to provide descriptive output about the running process. |
Details
There have been several attempts to adapt the k-means algorithm in the context of the statistical shape analysis, each one adapting a different version of the k-means algorithm (Amaral et al. (2010), Georgescu (2009)). In Vinue et al. (2014), it is demonstrated that the Lloyd k-means represents a noticeable reduction in the computation involved when the sample size increases, compared with the Hartigan-Wong k-means. We state that Hartigan-Wong should be used in the shape analysis context only for very small samples.
Value
A list with the following elements:
asig: Optimal clustering.
cases: Anthropometric cases (optimal centers).
vopt: Optimal objective function.
initials: Random initial values used in each iteration. These values are then used by HartiganShapes.
If a simulation study is carried out, the following elements are returned:
asig: Optimal clustering.
cases: Anthropometric cases (optimal centers).
vopt: Optimal objective function.
compTime: Computational time.
AllRate: Allocation rate.
initials: Random initial values used in each iteration. These values are then used by HartiganShapes.
Author(s)
Amelia Simo
References
Vinue, G., Simo, A., and Alemany, S., (2016). The k-means algorithm for 3D shapes with an application to apparel design, Advances in Data Analysis and Classification 10(1), 103–132.
Lloyd, S. P., (1982). Least Squares Quantization in PCM, IEEE Transactions on Information Theory 28, 129–137.
Dryden, I. L., and Mardia, K. V., (1998). Statistical Shape Analysis, Wiley, Chichester.
See Also
HartiganShapes, trimmedLloydShapes, landmarksSampleSpaSurv, cube8landm, parallelep8landm, cube34landm, parallelep34landm, procGPA, optraShapes, qtranShapes
Examples
#CLUSTERING INDIVIDUALS ACCORDING TO THEIR SHAPE:
landmarksNoNa <- na.exclude(landmarksSampleSpaSurv)
dim(landmarksNoNa)
#[1] 574 198
numLandmarks <- (dim(landmarksNoNa)[2]) / 3
#[1] 66
#As a toy example, only the first 10 individuals are used.
landmarksNoNa_First10 <- landmarksNoNa[1:10, ]
(numIndiv <- dim(landmarksNoNa_First10)[1])
#[1] 10
array3D <- array3Dlandm(numLandmarks, numIndiv, landmarksNoNa_First10)
#shapes::plotshapes(array3D[,,1])
#calibrate::textxy(array3D[,1,1], array3D[,2,1], labs = 1:numLandmarks, cex = 0.7)
numClust <- 2 ; algSteps <- 1 ; niter <- 1 ; stopCr <- 0.0001
resLL <- LloydShapes(array3D, numClust, algSteps, niter, stopCr, FALSE, FALSE)
asig <- resLL$asig
table(resLL$asig)
prototypes <- anthrCases(resLL)
#Note: For a simulation study, see www.uv.es/vivigui/softw/more_examples.R