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ddc  

Distance Density Clustering Algorithm
View on CRAN: Click here


Download and install ddc package within the R console
Install from CRAN:
install.packages("ddc")

Install from Github:
library("remotes")
install_github("cran/ddc")

Install by package version:
library("remotes")
install_version("ddc", "1.0.1")



Attach the package and use:
library("ddc")
Maintained by
Ruizhe Ma
[Scholar Profile | Author Map]
First Published: 2022-12-14
Latest Update: 2022-12-14
Description:
A distance density clustering (DDC) algorithm in R. DDC uses dynamic time warping (DTW) to compute a similarity matrix, based on which cluster centers and cluster assignments are found. DDC inherits dynamic time warping (DTW) arguments and constraints. The cluster centers are centroid points that are calculated using the DTW Barycenter Averaging (DBA) algorithm. The clustering process is divisive. At each iteration, cluster centers are updated and data is reassigned to cluster centers. Early stopping is possible. The output includes cluster centers and clustering assignment, as described in the paper (Ma et al (2017) ).
How to cite:
Ruizhe Ma (2022). ddc: Distance Density Clustering Algorithm. R package version 1.0.1, https://cran.r-project.org/web/packages/ddc. Accessed 21 Feb. 2025.
Previous versions and publish date:
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Other packages that cited ddc R package
View ddc citation profile
Other R packages that ddc depends, imports, suggests or enhances
Complete documentation for ddc
Functions, R codes and Examples using the ddc R package
Some associated functions: createDistMatrix . createLabelMatrix . createStandardMatrix . ddc . 
Some associated R codes: ddc-internal.R . ddc.R . utility-analysis.R . utility-cluster.R . utility-matrix.R . utility-seed.R .  Full ddc package functions and examples
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