Optimal Distance-Based Clustering for Multidimensional Data with Sequential Constraint

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 can find the optimal clustering if the number of clusters is known. Otherwise, methods and can be used.

Version: 1.0
Depends: R (≥ 2.10.0)
Published: 2015-05-04
Author: Tibor Szkaliczki [aut, cre], J. Song [ctb]
Maintainer: Tibor Szkaliczki <szkaliczki.tibor at>
License: LGPL (≥ 3)
NeedsCompilation: yes
CRAN checks: results


Reference manual:


Package source:
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64):, r-oldrel (arm64):, r-release (x86_64):, r-oldrel (x86_64):


Please use the canonical form to link to this page.