| Type: | Package |
| Title: | Distance Density Clustering Algorithm |
| Version: | 1.0.1 |
| Date: | 2022-12-13 |
| Maintainer: | Ruizhe Ma <maruizhe.cs@gmail.com> |
| 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) <doi:10.1109/ICDMW.2017.11>). |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Depends: | R (≥ 4.2) |
| Imports: | dtw (≥ 1.22), dtwclust (≥ 5.5), parallel (≥ 4.2), magrittr (≥ 2.0), utils |
| RoxygenNote: | 7.2.1 |
| Encoding: | UTF-8 |
| ByteCompile: | yes |
| BuildResaveData: | no |
| NeedsCompilation: | yes |
| Suggests: | knitr, rmarkdown, spelling, testthat (≥ 3.0.0) |
| Language: | en-US |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| Packaged: | 2022-12-14 04:11:40 UTC; bingjiang |
| Author: | Ruizhe Ma [cre, aut], Bing Jiang [aut] |
| Repository: | CRAN |
| Date/Publication: | 2022-12-14 12:00:05 UTC |
Create the dataframe of the Dissimilarity matrix
Description
Use the DTW to generate the matrix
Usage
createDistMatrix(standard_matrix, output_dir = NULL, mc.cores = 1, ...)
Arguments
standard_matrix |
the matrix genereated by function 'createStandardMatrix' |
output_dir |
the file to save the dissimilarity matrix data |
mc.cores |
the number of cores would be used in parallel |
... |
the same parameters which would be used in 'dtw' for calculating the distances of events |
Value
the matrix, which describes pairwise distinction between M objects. It is a square symmetrical 'MxM' matrix with the (ij)th element equal to the value of a chosen measure of distinction between the (i)th and the (j)th object.
Examples
original_data <- data.frame("1"=c(1, 2, 1), "2"=c(5,6,7),
"3"=c(4, 5, 8), "4"=c(3, 1, 9))
standard_matrix <- createStandardMatrix(data = original_data)
dist_matrix <- createDistMatrix(standard_matrix = standard_matrix)
Create the dataframe with event names and the related labels
Description
Create the dataframe with event names and the related labels
Usage
createLabelMatrix(data, output_dir = NULL)
Arguments
data |
data structure as the files in "UCR Time Series Classification Archive" |
output_dir |
the file to save the label matrix data |
Value
the dataframe, including event names and labels
Examples
original_data <- data.frame("1"=c(1, 2, 1), "2"=c(5,6,7),
"3"=c(4, 5, 8), "4"=c(3, 1, 9))
label_matrix <- createLabelMatrix(data = original_data)
Create the dataframe, only including the event data
Description
Create the dataframe, only including the event data
Usage
createStandardMatrix(data, output_dir = NULL)
Arguments
data |
data structure as the files in "UCR Time Series Classification Archive" |
output_dir |
the file to save the standard matrix data |
Value
the dataframe of event data
Examples
original_data <- data.frame("1"=c(1, 2, 1), "2"=c(5,6,7),
"3"=c(4, 5, 8), "4"=c(3, 1, 9))
standard_matrix <- createStandardMatrix(data = original_data)
Execute DDC to cluster the dataset
Description
Execute DDC to cluster the dataset
Usage
ddc(dist_matrix, standard_matrix, label_matrix, end_cluster_num = NULL, ...)
Arguments
dist_matrix |
the created dist matrix |
standard_matrix |
the original data matrix |
label_matrix |
the matrix including events and labels |
end_cluster_num |
the max number of cluster when the procedue ends |
... |
including: mc.cores(cores used in parallel), the dtw parameters like step.pattern, keep, mc.cores |
Value
the cluster array as a result, including 'Centroid', 'Elements' and 'DBAValue' for each cluster
Examples
original_data <- data.frame("1"=c(1, 2, 1), "2"=c(5,6,7),
"3"=c(4, 5, 8), "4"=c(3, 1, 9))
standard_matrix <- createStandardMatrix(data = original_data)
label_matrix <- createLabelMatrix(data = original_data)
dist_matrix <- createDistMatrix(standard_matrix = standard_matrix)
result <- ddc(dist_matrix=dist_matrix, standard_matrix=standard_matrix,
label_matrix=label_matrix, end_cluster_num=2)