| Name : R-clustering.sc.dp
| |
| Version : 1.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.1.7
| Date : 2024-06-14 11:05:20
|
| Group : Development/Libraries/Other
| Source RPM : R-clustering.sc.dp-1.1-lp153.1.7.src.rpm
|
| Size : 0.06 MB
| |
| Packager : (none)
| |
| Summary : Optimal Distance-Based Clustering for Multidimensional Data with Sequential Constraint
|
Description :
A dynamic programming algorithm for optimal clustering multidimensional
data with sequential constraint. The algorithm minimizes the sum of
squares of within-cluster distances. The sequential constraint allows
only subsequent items of the input data to form a cluster. The
sequential constraint is typically required in clustering data streams
or items with time stamps such as video frames, GPS signals of a
vehicle, movement data of a person, e-pen data, etc. The algorithm
represents an extension of \'Ckmeans.1d.dp\' to multiple dimensional
spaces. Similarly to the one-dimensional case, the algorithm guarantees
optimality and repeatability of clustering. Method clustering.sc.dp()
can find the optimal clustering if the number of clusters is known.
Otherwise, methods findwithinss.sc.dp() and backtracking.sc.dp() can be
used. See Szkaliczki, T. (2016) \"clustering.sc.dp: Optimal Clustering
with Sequential Constraint by Using Dynamic Programming\" < doi:
10.32614/RJ-2016-022> for more information.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
