Package: ddc 1.0.1
Ruizhe Ma
ddc: Distance Density Clustering Algorithm
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) <doi:10.1109/ICDMW.2017.11>).
Authors:
ddc_1.0.1.tar.gz
ddc_1.0.1.tar.gz(r-4.5-noble)ddc_1.0.1.tar.gz(r-4.4-noble)
ddc_1.0.1.tgz(r-4.4-emscripten)ddc_1.0.1.tgz(r-4.3-emscripten)
ddc.pdf |ddc.html✨
ddc/json (API)
| # Install 'ddc' in R: |
| install.packages('ddc', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:4162c3e80e. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 29 2024 |
| R-4.5-linux | OK | Oct 29 2024 |
Exports:createDistMatrixcreateLabelMatrixcreateStandardMatrixddc
Dependencies:base64encbslibcachemclasscliclueclustercodetoolscolorspacecommonmarkcrayondigestdplyrdtwdtwclustfansifarverfastmapflexclustfontawesomeforeachfsgenericsggplot2ggrepelgluegtablehtmltoolshttpuvisobanditeratorsjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemodeltoolsmunsellnlmepillarpkgconfigplyrpromisesproxyR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppThreadreshape2rlangRSpectrasassscalesshinyshinyjssourcetoolsstringistringrtibbletidyselectutf8vctrsviridisLitewithrxtable
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Create the dataframe of the Dissimilarity matrix | createDistMatrix |
| Create the dataframe with event names and the related labels | createLabelMatrix |
| Create the dataframe, only including the event data | createStandardMatrix |
| Execute DDC to cluster the dataset | ddc |