An parallelised implementation of a Jarn<U+00ED>k (Prim/Dijkstra)-like algorithm for determining a(*) minimum spanning tree (MST) of a complete undirected graph representing a set of n points with weights given by a pairwise distance matrix.
(*) Note that there might be multiple minimum trees spanning a given graph.
mst(d, ...)# S3 method for default
mst(
d,
distance = c("euclidean", "l2", "manhattan", "cityblock", "l1", "cosine"),
M = 1L,
cast_float32 = TRUE,
verbose = FALSE,
...
)
# S3 method for dist
mst(d, M = 1L, verbose = FALSE, ...)
either a numeric matrix (or an object coercible to one,
e.g., a data frame with numeric-like columns) or an
object of class dist, see dist.
further arguments passed to or from other methods.
metric used to compute the linkage, one of:
"euclidean" (synonym: "l2"),
"manhattan" (a.k.a. "l1" and "cityblock"),
"cosine".
smoothing factor; M = 1 gives the selected distance;
otherwise, the mutual reachability distance is used.
logical; whether to compute the distances using 32-bit instead of 64-bit precision floating-point arithmetic (up to 2x faster).
logical; whether to print diagnostic messages and progress information.
Matrix of class mst with n-1 rows and 3 columns:
from, to and dist. It holds from < to.
Moreover, dist is sorted nondecreasingly.
The i-th row gives the i-th edge of the MST.
(from[i], to[i]) defines the vertices (in 1,...,n)
and dist[i] gives the weight, i.e., the
distance between the corresponding points.
The method attribute gives the name of the distance used.
The Labels attribute gives the labels of all the input points.
If M > 1, the nn attribute gives the indices of the M-1
nearest neighbours of each point.
If d is a numeric matrix of size \(n p\),
the \(n (n-1)/2\) distances are computed on the fly, so that \(O(n M)\)
memory is used.
The algorithm is parallelised; set the OMP_NUM_THREADS environment
variable Sys.setenv to control the number of threads
used.
Time complexity is \(O(n^2)\) for the method accepting an object of
class dist and \(O(p n^2)\) otherwise.
If M >= 2, then the mutual reachability distance \(m(i,j)\) with smoothing
factor M (see Campello et al. 2015)
is used instead of the chosen "raw" distance \(d(i,j)\).
It holds \(m(i, j)=\max(d(i,j), c(i), c(j))\), where \(c(i)\) is
\(d(i, k)\) with \(k\) being the (M-1)-th nearest neighbour of \(i\).
This makes "noise" and "boundary" points being "pulled away" from each other.
Genie++ clustering algorithm (see gclust)
with respect to the mutual reachability distance gains the ability to
identify some observations are noise points.
Note that the case M = 2 corresponds to the original distance, but we are
determining the 1-nearest neighbours separately as well, which is a bit
suboptimal; you can file a feature request if this makes your data analysis
tasks too slow.
V. Jarn<U+00ED>k, O jist<U+00E9>m probl<U+00E9>mu minim<U+00E1>ln<U+00ED>m, Pr<U+00E1>ce Moravsk<U+00E9> P<U+0159><U+00ED>rodov<U+011B>deck<U+00E9> Spole<U+010D>nosti 6 (1930) 57<U+2013>63.
C.F. Olson, Parallel algorithms for hierarchical clustering, Parallel Comput. 21 (1995) 1313<U+2013>1325.
R. Prim, Shortest connection networks and some generalisations, Bell Syst. Tech. J. 36 (1957) 1389<U+2013>1401.
Campello R., Moulavi D., Zimek A., Sander J., Hierarchical density estimates for data clustering, visualization, and outlier detection, ACM Transactions on Knowledge Discovery from Data 10(1) (2015) 5:1<U+2013>5:51.
emst_mlpack() for a very fast alternative
in case of (very) low-dimensional Euclidean spaces (and M = 1).
# NOT RUN {
library("datasets")
data("iris")
X <- iris[1:4]
tree <- mst(X)
# }
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