spatialKMeans-methods {Cardinal} | R Documentation |
Performs spatially-aware (SA) or spatially-aware structurally-adaptive (SASA) clustering of imaging data. The data are first projected into an embedded feature space where spatial structure is maintained using the Fastmap algorithm, and then ordinary k-means clustering is performed on the projected dataset.
## S4 method for signature 'SImageSet' spatialKMeans(x, r = 1, k = 2, method = c("gaussian", "adaptive"), weights = 1, iter.max = 100, nstart = 100, algorithm = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"), ncomp = 20, ...)
x |
The imaging dataset to cluster. |
r |
The spatial neighborhood radius of nearby pixels to consider. This can be a vector of multiple radii values. |
k |
The number of clusters. This can be a vector to try different numbers of clusters. |
method |
The method to use to calculate the spatial smoothing kernels for the embedding. The 'gaussian' method refers to spatially-aware (SA) clustering, and 'adaptive' refers to spatially-aware structurally-adaptive (SASA) clustering. |
weights |
An optional vector of feature weights to be applied to the features during the clustering. |
iter.max |
The maximum number of k-means iterations. |
nstart |
The number of restarts for the k-means algorithm. |
algorithm |
The k-means algorithm to use. See |
ncomp |
The number of fastmap components to calculate. |
... |
Ignored. |
An object of class SpatialKMeans
, which is a ResultSet
, where each component of the resultData
slot contains at least the following components:
cluster
:A vector of integers indicating the cluster for each pixel in the dataset.
centers
:A matrix of cluster centers.
time
:The amount of time the algorithm took to run.
r
:The neighborhood spatial smoothing radius.
k
:The number of clusters.
method
:The method for calculating spatial distances.
weights
:The feature weights (defaults to 1s).
fastmap
:A list with components giving details of the Fastmap projection.
Kylie A. Bemis
Alexandrov, T., & Kobarg, J. H. (2011). Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering. Bioinformatics, 27(13), i230-i238. doi:10.1093/bioinformatics/btr246
Faloutsos, C., & Lin, D. (1995). FastMap: A Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets. Presented at the Proceedings of the 1995 ACM SIGMOD international conference on Management of data.
set.seed(1) data <- matrix(c(NA, NA, 1, 1, NA, NA, NA, NA, NA, NA, 1, 1, NA, NA, NA, NA, NA, NA, NA, 0, 1, 1, NA, NA, NA, NA, NA, 1, 0, 0, 1, 1, NA, NA, NA, NA, NA, 0, 1, 1, 1, 1, NA, NA, NA, NA, 0, 1, 1, 1, 1, 1, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, NA, NA, NA, 1, 1, NA, NA, NA, NA, NA, NA, 1, 1, NA, NA, NA, NA, NA), nrow=9, ncol=9) sset <- generateImage(data, range=c(200, 300), step=1) clust1 <- spatialKMeans(sset, r=c(1,2), k=c(2,3), method="gaussian") clust2 <- spatialKMeans(sset, r=c(1,2), k=c(2,3), method="adaptive")