| Title: | Clustering with Overlaps |
| Version: | 1.1.0 |
| Description: | Provide functions for overlaps clustering, fuzzy clustering and interval-valued data manipulation. The package implement the following algorithms: OKM (Overlapping Kmeans) from Cleuziou, G. (2007) <doi:10.1109/icpr.2008.4761079> ; NEOKM (Non-exhaustive overlapping Kmeans) from Whang, J. J., Dhillon, I. S., and Gleich, D. F. (2015) <doi:10.1137/1.9781611974010.105> ; Fuzzy Cmeans from Bezdek, J. C. (1981) <doi:10.1007/978-1-4757-0450-1> ; Fuzzy I-Cmeans from de A.T. De Carvalho, F. (2005) <doi:10.1016/j.patrec.2006.08.014>. |
| Depends: | R (≥ 3.2.3) |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.1 |
| Suggests: | testthat |
| SystemRequirements: | Gnu Scientific Library version >= 1.12 |
| NeedsCompilation: | yes |
| Packaged: | 2024-11-13 11:02:19 UTC; hiot |
| Author: | Guillaume Cleuziou [aut],
Nicolas Hiot 
[cre] |
| Maintainer: | Nicolas Hiot <nicolas.hiot@univ-orleans.fr> |
| Repository: | CRAN |
| Date/Publication: | 2024-11-13 13:00:22 UTC |
Converts an interval object to an array representation.
Description
Converts an interval object to an array representation.
Usage
## S3 method for class 'interval'
as.array(x, ...)
Arguments
x |
An interval object to be converted. |
... |
Additional arguments to be passed to as.array(). |
Value
An array representation of the interval.
Examples
as.array(inter_city)
Converts an interval object to a data frame representation.
Description
Converts an interval object to a data frame representation.
Usage
## S3 method for class 'interval'
as.data.frame(x, ...)
Arguments
x |
An interval object to be converted. |
... |
dditional arguments to be passed to as.data.frame(). |
Value
A data frame representation of the interval.
Examples
as.data.frame(inter_city)
A generic function to convert various R objects into interval objects.
Description
A generic function to convert various R objects into interval objects.
Usage
as.interval(x)
Arguments
x |
An R object to be converted to an interval. |
Value
An interval object constructed from the R object or NULL if the type is not supported.
Converts an array to an interval object.
Description
The array must have three dimensions, with the second dimension of size 2, representing the minimum and maximum values.
Usage
## S3 method for class 'array'
as.interval(x)
Arguments
x |
An array to be converted to an interval object. |
Value
An interval object constructed from the array if it meets the requirements, otherwise attempts to convert it to a matrix first.
Examples
as.interval(array(1:12, dim = c(2, 2, 3)))
Provides a default method for converting unsupported data types to interval.
Description
Provides a default method for converting unsupported data types to interval.
Usage
## Default S3 method:
as.interval(x)
Arguments
x |
An object that does not have a supported conversion method. |
Value
'NULL', indicating no conversion is possible.
Identity Conversion for Interval
Description
Identity Conversion for Interval
Usage
## S3 method for class 'interval'
as.interval(x)
Arguments
x |
An interval object. |
Value
The input interval object, without modification.
Examples
as.interval(inter_city)
Converts a matrix to an interval object.
Description
The number of columns in the matrix must be even, representing pairs of minimum and maximum values.
Usage
## S3 method for class 'matrix'
as.interval(x)
Arguments
x |
A matrix where each pair of columns represents the minimum and maximum bounds of intervals. |
Value
An interval object constructed from the matrix.
Examples
as.interval(matrix(1:12, 3, 4))
Converts a numeric vector to an interval object.
Description
The length of the numeric vector must be even, representing pairs of minimum and maximum values.
Usage
## S3 method for class 'numeric'
as.interval(x)
Arguments
x |
A numeric vector where each consecutive pair of values represents an interval. |
Value
An interval object constructed from the numeric vector.
Examples
as.interval(1:6)
Converts an interval object to a matrix representation.
Description
Each interval is expanded into its minimum and maximum bounds.
Usage
## S3 method for class 'interval'
as.matrix(x, ...)
Arguments
x |
An interval object to be converted. |
... |
Additional arguments to be passed to as.vector(). |
Value
A matrix representation of the interval, with two columns for each interval's minimum and maximum values.
Examples
as.matrix(inter_city)
Converts an interval object to its vector representation.
Description
Converts an interval object to its vector representation.
Usage
## S3 method for class 'interval'
as.vector(x, ...)
Arguments
x |
An interval object to be converted. |
... |
Additional arguments to be passed to as.vector(). |
Value
A numeric vector where each consecutive pair of values represents an interval.
Examples
as.vector(inter_city)
Generate Colors for Clustering Visualization
Description
Generates a color for each data point based on its clustering assignment, facilitating visual distinction of clusters in plots.
Usage
cluster_color(x)
Arguments
x |
A clustering vector or a matrix. If a vector is provided, it represents the cluster assignments for each data point. If a matrix is provided, each row should represent a data point's membership across multiple clusters. |
Value
A character vector of colors (in hexadecimal format) corresponding to the clustering assignments, suitable for use in plotting functions.
Examples
plot(iris[, 1:2], col = cluster_color(neokm(iris, 2, 0.2, 0.05)$cluster))
Transforms a matrix of membership degrees into a logical matrix based on a specified threshold.
Description
Transforms a matrix of membership degrees into a logical matrix based on a specified threshold.
Usage
degree2logical(x, t = min(apply(x, 1, max)))
Arguments
x |
A matrix of membership degrees. |
t |
Threshold value for converting the degrees to logical values. By default, it uses the minimum of the maximum values in each row. |
Value
A logical matrix where each element is 'TRUE' if it meets or exceeds the threshold, and 'FALSE' otherwise.
Examples
degrees <- matrix(runif(9), nrow = 3)
degree2logical(degrees, t = 0.5)
Performs fuzzy c-means clustering on interval data, allowing for soft clustering of data points into multiple clusters.
Description
Performs fuzzy c-means clustering on interval data, allowing for soft clustering of data points into multiple clusters.
Usage
fuzzy_icmeans(
x,
centers,
m = 2,
nstart = 2,
distance = "euclid",
trace = FALSE,
iter.max = 40
)
Arguments
x |
A 3D interval array representing the data to be clustered. |
centers |
Either the number of clusters or a set of pre-initialized cluster centers. If a number is provided, it specifies how many clusters to create. |
m |
A number greater than 1 that controls the degree of fuzziness in the clustering process (default is 2). |
nstart |
Number of times to run the clustering algorithm with different starting values to find the best solution (default is 2). |
distance |
A string specifying the distance metric to use, either 'euclid' for Euclidean distance or 'hausdorff' for Hausdorff distance (default is 'euclid'). |
trace |
Logical, if 'TRUE', tracing information on the progress of the algorithm is displayed (default is 'FALSE'). |
iter.max |
Maximum number of iterations allowed for the clustering algorithm (default is 40). |
Value
A list of clustering results, including: - 'cluster': The membership matrix indicating the degree of belonging of each data point to each cluster. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': Sizes of each cluster. - 'iter': Number of iterations run by the algorithm. - 'overlaps': The average overlap among clusters.
Examples
fuzzy_icmeans(iaggregate(iris, col = 5), 2)
fuzzy_icmeans(iaggregate(iris, col = 5), iaggregate(iris, col = 5))
Aggregates data into a 3D interval array based on a specified column.
Description
Aggregates data into a 3D interval array based on a specified column.
Usage
iaggregate(data, col = 1)
Arguments
data |
The data frame to aggregate. |
col |
The index of the column to aggregate by. |
Value
A structured interval object representing the aggregated data.
Examples
iaggregate(iris, col = 5)
iaggregate(rock, col = 4)
iaggregate(cars, col = 1)
Combines multiple interval objects into a single interval object.
Description
Combines multiple interval objects into a single interval object.
Usage
ibind(..., class = FALSE)
Arguments
... |
Interval objects to bind together. |
class |
Logical value indicating whether to assign a new class label to each interval object when binding. If 'TRUE', each set of intervals will have a distinct class label. |
Value
A new interval object containing the combined intervals from the input objects.
Examples
ibind(iaggregate(iris, 5), iaggregate(iris, 5))
ibind(iaggregate(iris, 5), iaggregate(iris, 5), iaggregate(iris, 5),
class = TRUE)
Creates intervals from Normal Distribution using specified mean and standard deviation values for both the center and half-size of the intervals.
Description
Creates intervals from Normal Distribution using specified mean and standard deviation values for both the center and half-size of the intervals.
Usage
igenerate(n, ...)
Arguments
n |
Number of intervals to generate. |
... |
Vectors representing parameters for generating intervals: each vector should contain four values ('center mean', 'center sd', 'half-size mean', 'half-size sd'). |
Value
An interval object containing the generated intervals.
Examples
igenerate(1, c(0, 1, 2, 1))
igenerate(1, c(0, 1, 2, 1), c(100, 1, 2, 1))
Performs k-means clustering on interval data, allowing for partitioning of data points into distinct clusters.
Description
Performs k-means clustering on interval data, allowing for partitioning of data points into distinct clusters.
Usage
ikmeans(
x,
centers,
nstart = 10,
distance = "euclid",
trace = FALSE,
iter.max = 20
)
Arguments
x |
A 3D interval array representing the data to be clustered. |
centers |
Either the number of clusters to create or a set of pre-initialized cluster centers. If a number is provided, it specifies how many clusters to create. |
nstart |
The number of times to run the k-means algorithm with different starting values in order to find the best solution (default is 10). |
distance |
A string specifying the distance metric to use: 'euclid' for Euclidean distance or 'hausdorff' for Hausdorff distance (default is 'euclid'). |
trace |
Logical value indicating whether to show progress of the algorithm (default is 'FALSE'). |
iter.max |
Maximum number of iterations allowed for the k-means algorithm (default is 20). |
Value
A list of clustering results, including: - 'cluster': A vector indicating the cluster assignment of each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': The number of points in each cluster. - 'iter': Number of iterations the algorithm executed.
Examples
ikmeans(iaggregate(iris, col = 5), 2)
ikmeans(iaggregate(iris, col = 5), iaggregate(iris, col = 5))
Performs clustering on interval data using the Neo-KM algorithm, which allows for overlapping and non-exhaustive cluster membership.
Description
Performs clustering on interval data using the Neo-KM algorithm, which allows for overlapping and non-exhaustive cluster membership.
Usage
ineokm(
x,
centers,
alpha = 0.3,
beta = 0.05,
nstart = 10,
trace = FALSE,
iter.max = 20
)
Arguments
x |
A 3D interval array representing the data to be clustered. |
centers |
Either the number of clusters to create or a set of pre-initialized cluster centers. If a number is provided, it specifies how many clusters to create. |
alpha |
A numeric value that controls the degree of overlap between clusters (default is 0.3). |
beta |
A numeric value that controls the non-exhaustiveness of clusters (default is 0.05). |
nstart |
The number of times to run the Neo-KM algorithm with different starting values in order to find the best solution (default is 10). |
trace |
Logical value indicating whether to show the progress of the algorithm (default is 'FALSE'). |
iter.max |
Maximum number of iterations allowed for the Neo-KM algorithm (default is 20). |
Value
A list of clustering results, including: - 'cluster': A vector indicating the cluster assignment of each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': The number of points in each cluster. - 'iter': Number of iterations the algorithm executed.
Examples
ineokm(iaggregate(iris, col = 5), 3)
ineokm(iaggregate(iris, col = 5), iaggregate(iris, col = 5), 1, 2)
Temperature by month and humidity in european city.
Description
Temperature by month and humidity in european city.
Usage
inter_city
Format
A interval structure with 68 rows and 13 variables divided in min and max with 17 class:
- temp.jan
- temp.fev
- temp.mars
- temp.avr
- temp.mai
- temp.juin
- temp.juil
- temp.aout
- temp.sep
- temp.oct
- temp.nov
- temp.dec
- humid
Class :
- Allemagne
- Angleterre
- Autriche
- Belgique
- Bulgarie
- Croatie
- Danemark
- Espagne
- France
- Italie
- Pays-Bas
- Pologne
- Portugal
- Roumanie
- Russie
- Turquie
- Ukraine
Emotions in music aggregate on BPM to interval multi label data.
Description
Emotions in music aggregate on BPM to interval multi label data.
Usage
inter_emotions
Format
A interval structure with 59 rows and 71 variables divided in min and max with 6 class:
- Mean_Acc1298_Mean_Mem40_Centroid
- Mean_Acc1298_Mean_Mem40_Rolloff
- Mean_Acc1298_Mean_Mem40_Flux
- Mean_Acc1298_Mean_Mem40_MFCC_0
...
- Mean_Acc1298_Mean_Mem40_MFCC_12
- Mean_Acc1298_Std_Mem40_Centroid
- Mean_Acc1298_Std_Mem40_Rolloff
- Mean_Acc1298_Std_Mem40_Flux
- Mean_Acc1298_Std_Mem40_MFCC_0
...
- Mean_Acc1298_Std_Mem40_MFCC_12
- Std_Acc1298_Mean_Mem40_Centroid
- Std_Acc1298_Mean_Mem40_Rolloff
- Std_Acc1298_Mean_Mem40_Flux
- Std_Acc1298_Mean_Mem40_MFCC_0
...
- Std_Acc1298_Mean_Mem40_MFCC_12
- Std_Acc1298_Std_Mem40_Centroid
- Std_Acc1298_Std_Mem40_Rolloff
- Std_Acc1298_Std_Mem40_Flux
- Std_Acc1298_Std_Mem40_MFCC_0
...
- Std_Acc1298_Std_Mem40_MFCC_12
- BH_LowPeakAmp
- BH_LowPeakBPM
- BH_HighPeakAmp
- BH_HighLowRatio
- BHSUM1
- BHSUM2
- BHSUM3
Class :
- amazed.suprised
- happy.pleased
- relaxing.calm
- quiet.still
- sad.lonely
- angry.aggresive
Source
https://mulan.sourceforge.net/datasets-mlc.html
Results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars, aggregate on sulfur dioxide to interval simple label data.
Description
Results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars, aggregate on sulfur dioxide to interval simple label data.
Usage
inter_wine
Format
A interval structure with 132 rows and 10 variables divided in min and max with 7 class:
- fixed.acidity
- volatile.acidity
- citric.acid
- residual.sugar
- chlorides
- total.sulfur.dioxide
- density
- pH
- sulphates
- alcohol
Class :
- Class3
- Class4
- Class5
- Class6
- Class7
- Class8
- Class9
Source
https://archive.ics.uci.edu/dataset/186/wine+quality
Clusters interval data using the OKM (Overlapping K-means) algorithm.
Description
Clusters interval data using the OKM (Overlapping K-means) algorithm.
Usage
iokm(
x,
centers,
nstart = 10,
distance = "euclid",
algorithm = "std",
update = "mean",
trace = FALSE,
iter.max = 20,
secure = FALSE
)
Arguments
x |
A 3D interval array representing the data to be clustered. |
centers |
Either the number of clusters to create or a set of pre-initialized cluster centers. If a number is provided, it indicates how many clusters to create. |
nstart |
The number of times to run the OKM algorithm with different starting values to find the best result (default is 10). |
distance |
A string specifying the distance metric to use: 'euclid' for Euclidean distance or 'hausdorff' for Hausdorff distance (default is 'euclid'). |
algorithm |
A string specifying the algorithm type to use: 'std' for the standard algorithm or 'matrix' for matrix-based algorithm (default is 'std'). |
update |
A string specifying the update method for cluster centers. Either: 'mean', 'sum', 'join' or 'meet' (default is 'mean'). |
trace |
Logical value indicating whether to show progress of the algorithm (default is 'FALSE'). |
iter.max |
Maximum number of iterations allowed for the OKM algorithm (default is 20). |
secure |
Logical value indicating whether to ensure that the minimum is less than or equal to the maximum in intervals (default is 'FALSE'). |
Value
A list of clustering results, including: - 'cluster': Matrix indicating the cluster assignment for each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by elements. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': The number of points in each cluster. - 'iter': The number of iterations the algorithm executed. - 'overlaps': The average overlap across clusters.
Examples
iokm(iaggregate(iris, col = 5), 2)
iokm(iaggregate(iris, col = 5), iaggregate(iris, col = 5))
Determines if an object is a strictly valid interval object.
Description
Determines if an object is a strictly valid interval object.
Usage
is.interval(x)
Arguments
x |
An R object to be tested. |
Value
A logical value indicating whether the object is a valid interval.
Examples
is.interval(inter_city)
is.interval(1:4)
Plots the overlap of membership degrees in a matrix as a function of a threshold.
Description
Plots the overlap of membership degrees in a matrix as a function of a threshold.
Usage
measure(x, min = 0, max = 1, step = 0.1)
Arguments
x |
A matrix of membership degrees. |
min |
Minimum threshold value for the plot (default is 0). |
max |
Maximum threshold value for the plot (default is 1). |
step |
Step size for the threshold values (default is 0.1). |
Value
No return value, it plot the overlap as a function of the threshold.
Examples
membership_matrix <- matrix(runif(20), nrow = 5)
measure(membership_matrix, min = 0, max = 1, step = 0.2)
Clusters data using the NEOKM (Non-Exhaustive Overlapping K-means) algorithm.
Description
Clusters data using the NEOKM (Non-Exhaustive Overlapping K-means) algorithm.
Usage
neokm(
x,
centers,
alpha = 0.3,
beta = 0.05,
nstart = 10,
trace = FALSE,
iter.max = 20
)
Arguments
x |
A numeric matrix or data frame containing the data to be clustered. |
centers |
Either the number of clusters to create or a set of pre-initialized cluster centers. If a number is provided, it indicates how many clusters to create. |
alpha |
A numeric value representing the degree of overlap allowed between clusters (default is 0.3). |
beta |
A numeric value representing non-exhaustiveness, which affects the cluster formation (default is 0.05). |
nstart |
The number of times to run the NEOKM algorithm with different starting values to find the best result (default is 10). |
trace |
Logical value indicating whether to show progress of the algorithm (default is 'FALSE'). |
iter.max |
Maximum number of iterations allowed for the NEOKM algorithm (default is 20). |
Value
A list of clustering results, including: - 'cluster': Matrix indicating the cluster assignment for each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by elements. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': The number of points in each cluster. - 'iter': The number of iterations the algorithm executed. - 'overlaps': The average overlap across clusters.
Examples
neokm(iris[, -5], 3)
neokm(iris[, -5], iris[, -5], 1, 2)
Clusters data using the OKM (Overlapping K-Means) clustering algorithm.
Description
Clusters data using the OKM (Overlapping K-Means) clustering algorithm.
Usage
okm(x, centers, iter.max = 10, nstart = 1, trace = FALSE, method = "euclid")
Arguments
x |
A numeric data matrix or data frame containing the data to be clustered. |
centers |
Either a positive integer indicating the number of clusters to create or a matrix of pre-initialized cluster centers. |
iter.max |
Maximum number of iterations allowed for the clustering algorithm (default is 10). |
nstart |
Number of random initializations to find the best result (default is 1). |
trace |
Logical value indicating whether to display the progress of the algorithm (default is 'FALSE'). |
method |
A string specifying the distance metric to use; options are 'euclid' (Euclidean distance) or 'manhattan' (Manhattan distance) (default is "euclid"). |
Value
A list containing the clustering results, including: - 'cluster': Matrix indicating the cluster assignments for each data point. - 'centers': The final cluster centers. - 'tot.withinss': Total within-cluster sum of squares. - 'overlaps': The measure of overlap among clusters.
Examples
okm(iris[, -5], 3)
Generates a visual representation of interval data as rectangles on a plot.
Description
Generates a visual representation of interval data as rectangles on a plot.
Usage
## S3 method for class 'interval'
plot(x, ...)
Arguments
x |
An interval object to be plotted. |
... |
Additional graphical parameters such as 'col' and 'add'. |
Value
No return value, it plot the interval.
Examples
plot(iaggregate(iris, 5))
plot(iaggregate(iris, 5), col = 4)
plot(iaggregate(iris, 5), add = TRUE)
Displays the results of fuzzy icmeans clustering in a readable format.
Description
Displays the results of fuzzy icmeans clustering in a readable format.
Usage
## S3 method for class 'icmeans'
print(x, ...)
Arguments
x |
An 'icmeans' object resulting from the 'fuzzy_icmeans' function. |
... |
Additional arguments passed to print(). |
Value
No return value, it prints the clustering results to the console.
Displays the results of ikmeans clustering in a readable format.
Description
Displays the results of ikmeans clustering in a readable format.
Usage
## S3 method for class 'ikmeans'
print(x, ...)
Arguments
x |
An 'ikmeans' object resulting from the 'ikmeans' function. |
... |
Additional arguments passed to print(). |
Value
No return value, it prints the clustering results to the console.
Displays the results of Neo-KM clustering in a user-friendly format.
Description
Displays the results of Neo-KM clustering in a user-friendly format.
Usage
## S3 method for class 'ineokm'
print(x, ...)
Arguments
x |
An 'ineokm' object resulting from the 'ineokm' function. |
... |
Additional arguments passed to print(). |
Value
No return value, it prints the clustering results to the console.
Custom print method for displaying interval objects in a readable format.
Description
Custom print method for displaying interval objects in a readable format.
Usage
## S3 method for class 'interval'
print(x, ...)
Arguments
x |
An interval object to be printed. |
... |
Additional arguments passed to the underlying print() function. |
Value
No return value, it prints the interval to the console.
Examples
print(inter_city)
Displays the results of IOKM clustering in a user-friendly format.
Description
Displays the results of IOKM clustering in a user-friendly format.
Usage
## S3 method for class 'iokm'
print(x, ...)
Arguments
x |
An 'iokm' object resulting from the 'iokm' function. |
... |
Additional arguments passed to print(). |
Value
No return value, it prints the clustering results to the console.
Displays the results of NEOKM clustering in a user-friendly format.
Description
Displays the results of NEOKM clustering in a user-friendly format.
Usage
## S3 method for class 'neokm'
print(x, ...)
Arguments
x |
A 'neokm' object resulting from the 'neokm' function. |
... |
Additional arguments passed to print(). |
Value
No return value, it prints the clustering results to the console.
Displays the results of OKM clustering in a readable format.
Description
Displays the results of OKM clustering in a readable format.
Usage
## S3 method for class 'okm'
print(x, ...)
Arguments
x |
An OKM object resulting from the 'okm' function. |
... |
Additional arguments passed to print(). |
Value
No return value, it prints the clustering results to the console.
Displays the results of R1-OKM clustering in a readable format.
Description
Displays the results of R1-OKM clustering in a readable format.
Usage
## S3 method for class 'r1okm'
print(x, ...)
Arguments
x |
An R1-OKM object resulting from the 'r1okm' function. |
... |
Additional arguments passed to print(). |
Value
No return value, it prints the clustering results to the console.
Displays the results of R2-OKM clustering in a readable format.
Description
Displays the results of R2-OKM clustering in a readable format.
Usage
## S3 method for class 'r2okm'
print(x, ...)
Arguments
x |
An R2-OKM object resulting from the 'r2okm' function. |
... |
Additional arguments passed to print(). |
Value
No return value, it prints the clustering results to the console.
Cluster data using the R1-OKM algorithm.
Description
Cluster data using the R1-OKM algorithm.
Usage
r1okm(x, centers, alpha = 0, nstart = 10, trace = FALSE, iter.max = 20)
Arguments
x |
A numeric data matrix or data frame containing the data to be clustered. |
centers |
Either a positive integer indicating the number of clusters to create or a matrix of initial cluster centers. |
alpha |
A numeric parameter controlling the clustering behavior, influencing the degree of overlap between clusters (default is 0). |
nstart |
Number of random initializations to find the best clustering result (default is 10). |
trace |
Logical value indicating whether to display progress information during execution (default is 'FALSE'). |
iter.max |
Maximum number of iterations allowed for the clustering algorithm (default is 20). |
Value
A list containing the clustering results, including: - 'cluster': Matrix indicating the cluster assignments for each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares for each cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': Number of data points in each cluster. - 'iter': Number of iterations performed. - 'overlaps': Average number of clusters that each point overlaps with.
Examples
r1okm(iris[, -5], 3)
r1okm(iris[, -5], 3, alpha = -0.5)
r1okm(iris[, -5], iris[, -5], alpha = 1)
Cluster data using the R2-OKM algorithm.
Description
Cluster data using the R2-OKM algorithm.
Usage
r2okm(x, centers, lambda = 0, nstart = 10, trace = FALSE, iter.max = 20)
Arguments
x |
A numeric data matrix or data frame containing the data to be clustered. |
centers |
Either a positive integer specifying the number of clusters to create or a matrix of initial cluster centers. |
lambda |
A numeric parameter that controls the clustering behavior, influencing the shape and separation of clusters (default is 0). |
nstart |
Number of random initializations to find the best clustering result (default is 10). |
trace |
Logical value indicating whether to display progress information during execution (default is 'FALSE'). |
iter.max |
Maximum number of iterations allowed for the clustering algorithm (default is 20). |
Value
A list containing the clustering results, which includes: - 'cluster': Matrix indicating the cluster assignments for each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares for each cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': Number of data points in each cluster. - 'iter': Number of iterations performed. - 'overlaps': Average number of clusters that each point overlaps with.
Examples
r2okm(iris[, -5], 3)
r2okm(iris[, -5], 3, lambda = 0.3)
r2okm(iris[, -5], iris[, -5], lambda = 1)
Reads a CSV file and converts the data into a 3D interval array.
Description
Reads a CSV file and converts the data into a 3D interval array.
Usage
read.interval(..., row.names = FALSE, class = NULL)
Arguments
... |
Additional arguments passed to read.csv(). |
row.names |
Logical indicating if the first column contains row names. |
class |
The column index of class labels (set to 'NULL' if not present). |
Value
A structured interval object representing the data from the CSV file.
Writes an interval object to a CSV file.
Description
Writes an interval object to a CSV file.
Usage
write.interval(x, ..., class = FALSE)
Arguments
x |
An interval object to be saved. |
... |
Additional arguments passed to write.csv(). |
class |
Logical indicating whether to add the class column in the CSV. |
Value
No return value, it saves the interval to the given CSV file.