| Title: | Change Points Detection with Spectral Clustering |
| Version: | 1.0.0 |
| Description: | Calculate change point based on spectral clustering with the option to automatically calculate the number of clusters if this information is not available. |
| Depends: | R (≥ 3.4.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | stats, abind |
| RoxygenNote: | 6.1.0 |
| NeedsCompilation: | no |
| Packaged: | 2018-10-14 17:16:39 UTC; uzai_ |
| Author: | Luis Uzai [aut, cre] |
| Maintainer: | Luis Uzai <uzai_ff@hotmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2018-10-19 14:20:03 UTC |
DEVICE1
Description
Derivation of RefrigerationDevices of the UCR Time Series Classification Repository These problems were taken from data recorded as part of government sponsored study called Powering the Nation. The intention was to collect behavioural data about how consumers use electricity within the home to help reduce the UK's carbon footprint.
Usage
DEVICE1
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
DEVICE2
Description
Derivation of RefrigerationDevices of the UCR Time Series Classification Repository These problems were taken from data recorded as part of government sponsored study called Powering the Nation. The intention was to collect behavioural data about how consumers use electricity within the home to help reduce the UK's carbon footprint.
Usage
DEVICE2
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
DEVICE3
Description
Derivation of RefrigerationDevices of the UCR Time Series Classification Repository These problems were taken from data recorded as part of government sponsored study called Powering the Nation. The intention was to collect behavioural data about how consumers use electricity within the home to help reduce the UK's carbon footprint.
Usage
DEVICE3
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
DEVICE4
Description
Derivation of RefrigerationDevices of the UCR Time Series Classification Repository These problems were taken from data recorded as part of government sponsored study called Powering the Nation. The intention was to collect behavioural data about how consumers use electricity within the home to help reduce the UK's carbon footprint.
Usage
DEVICE4
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
DEVICE5
Description
Derivation of RefrigerationDevices of the UCR Time Series Classification Repository These problems were taken from data recorded as part of government sponsored study called Powering the Nation. The intention was to collect behavioural data about how consumers use electricity within the home to help reduce the UK's carbon footprint.
Usage
DEVICE5
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
DEVICE6
Description
Derivation of RefrigerationDevices of the UCR Time Series Classification Repository These problems were taken from data recorded as part of government sponsored study called Powering the Nation. The intention was to collect behavioural data about how consumers use electricity within the home to help reduce the UK's carbon footprint.
Usage
DEVICE6
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
FTIR1
Description
Derivation of Meat of the UCR Time Series Classification Repository Food spectrographs are used in chemometrics to classify food types, a task that has obvious applications in food safety and quality assurance. The classes are chicken, pork and turkey.
Usage
FTIR1
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
FTIR2
Description
Derivation of Meat of the UCR Time Series Classification Repository Food spectrographs are used in chemometrics to classify food types, a task that has obvious applications in food safety and quality assurance. The classes are chicken, pork and turkey.
Usage
FTIR2
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
FTIR3
Description
Derivation of Meat of the UCR Time Series Classification Repository Food spectrographs are used in chemometrics to classify food types, a task that has obvious applications in food safety and quality assurance. The classes are chicken, pork and turkey.
Usage
FTIR3
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
FTIR4
Description
Derivation of Meat of the UCR Time Series Classification Repository Food spectrographs are used in chemometrics to classify food types, a task that has obvious applications in food safety and quality assurance. The classes are chicken, pork and turkey.
Usage
FTIR4
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
FTIR5
Description
Derivation of Meat of the UCR Time Series Classification Repository Food spectrographs are used in chemometrics to classify food types, a task that has obvious applications in food safety and quality assurance. The classes are chicken, pork and turkey.
Usage
FTIR5
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
FTIR6
Description
Derivation of Meat of the UCR Time Series Classification Repository Food spectrographs are used in chemometrics to classify food types, a task that has obvious applications in food safety and quality assurance. The classes are chicken, pork and turkey.
Usage
FTIR6
Format
The format is: Value Class 1.063400 1 -0.953410 1 ... -0.596090 2 ...
Calculate change points with spectral cluster
Description
Calculate change point based on spectral clustering you have the option to automatically calculate the number of clusters if this information is not available
Usage
Spec(data, neighboorsNumber = 5, tolerance = 0.01,
maxNumberOfChangePoints = 19, estimationChangePointsNumber = NULL)
Arguments
data |
List of values corresponding to the time series |
neighboorsNumber |
Number of neighbors to consider affinity between nodes |
tolerance |
approximation to consider valid clusters, used only for calculation of forecast of change points, default 0.01 |
maxNumberOfChangePoints |
maximum number of clusters for prediction : default 19 |
estimationChangePointsNumber |
predicted number of change points in the series, if null, is automatically calculated: default null |
Details
Calculate change point based on spectral clustering you have the option to automatically calculate the number of clusters if this information is not available. It uses the Gaussian Kernel for the calculation of affinity matrix and Kmeans for the spectral cluster, however, several other options can be used and the package must be customized to better suit the use.
Value
Numerical array with the position of the change points in the time series
Author(s)
Luis Gustavo Uzai
Examples
data <- DEVICE1[, 1]
realChangePoints <- c(which(diff(DEVICE1$Class) != 0))
calculateChangePoints <- Spec(data, neighboorsNumber = 6,
tolerance = 0.005, estimationChangePointsNumber = 2)
minValue <- -99999
maxValue <- 99999
plot(data, type = "l", xlab = "x", ylab = "y")
for (r in 1:length(realChangePoints)) {
lines(x = c(realChangePoints[r], realChangePoints[r]),
y = c(minValue, maxValue), lwd = 2, col = "red")
}
for (n in 1:length(calculateChangePoints)) {
lines(x = c(calculateChangePoints[n], calculateChangePoints[n]),
y = c(minValue, maxValue), lwd = 2, col = "blue")
}
Calculate the affinity matrix based on the similarity matrix
Description
Calculate the affinity matrix based on the similarity matrix
Usage
calculateAffinityMatrix(similarityMatrix, neighboorsNumber = 2)
Arguments
similarityMatrix |
Matrix of similarity between all points in the time series |
neighboorsNumber |
Number of neighbors to consider affinity between nodes |
Details
Calculate the affinity matrix based on the similarity matrix If the number of neighbors is equal to or greater than the similarity matrix then the similarity and affinity matrix are equal
Value
Affinity matrix based on the similarity matrix
Author(s)
Luis Gustavo Uzai
Estimate the number of possible clusters
Description
Adaptation of the bartlett method of the speccalt package to estimate the number of clusters in the context of spectral clustering to detect change points
Usage
clusterEstimatetNumber(eigenvectorValues, tolerance, maxClusterNumber)
Arguments
eigenvectorValues |
Eigenvector matrix based on the affinity matrix |
tolerance |
approximation to consider valid clusters |
maxClusterNumber |
maximum number of calculable clusters |
Details
Adaptation of the bartlett method of the speccalt package to estimate the number of clusters in the context of spectral clustering to detect change points
Value
An estimated number of clusters
Author(s)
Luis Gustavo Uzai
Converts the time series to position and value matrix
Description
Converts the time series to position and value matrix
Usage
convertToMatrixTimeSeries(data)
Arguments
data |
List of values corresponding to the time series |
Details
Gets a list of values of any size and creates a key and value array of all positions
Value
The key matrix and value of the time series.
Author(s)
Luis Gustavo Uzai
Calculate Gaussian Kernel
Description
Measure of similarity between two points represented by x1 and x2
Usage
gaussianKernel(x1, x2, alpha = 1)
Arguments
x1 |
first valor to computate |
x2 |
second valor to computate |
alpha |
Alpha Measure |
Details
Measure of similarity between two points represented by x1 and x2
Value
Measure of similarity between two points.
Author(s)
Luis Gustavo Uzai
Calculate the eigenvector of the affinity matrix
Description
Calculate the eigenvector of the affinity matrix
Usage
generateEigenvectorMatrix(affinityMatrix)
Arguments
affinityMatrix |
Affinity matrix based on the similarity matrix based on key and value matrix of the time series |
Details
Calculates the laplacian matrix based on the affinity matrix and calculates the auto values of the graph with the eigen function
Value
Eigenvector matrix based on the affinity matrix
Author(s)
Luis Gustavo Uzai
Calculate Similarity Matrix
Description
Use some similarity measure to calculate the similarity matrix
Usage
generateSimilarityMatrix(data, similarityMeasure)
Arguments
data |
Key and value matrix of a time series |
similarityMeasure |
Measure of similarity between two points represented by x1 and x2 |
Details
Use some similarity measure to calculate the similarity matrix
Value
Matrix of similarity calculated from the key and value matrix.
Author(s)
Luis Gustavo Uzai
Get the Factor of the cluster position in relation to the matrix of eigenvectors
Description
Get the Factor of the cluster position in relation to the matrix of eigenvectors
Usage
getClusterFact(eigenvectorValues, eigenvectorLengthLessOne, clusterNumber,
reverseClusterNumber)
Arguments
eigenvectorValues |
Eigenvector matrix based on the affinity matrix |
eigenvectorLengthLessOne |
the eigenvector matrix size minus 1 |
clusterNumber |
the cluster position number being tested |
reverseClusterNumber |
the number of the inverse position of the cluster being tested |
Details
Gets the factor of the value and its opposite in relation to the matrix of the eigenvectors
Value
Factor of the cluster position in relation to the matrix of eigenvectors
Author(s)
Luis Gustavo Uzai
Get the Product of the cluster position in relation to the matrix of eigenvectors
Description
Get the Product of the cluster position in relation to the matrix of eigenvectors
Usage
getClusterProd(eigenvectorValues, eigenvectorLengthLessOne, clusterNumber,
reverseClusterNumber)
Arguments
eigenvectorValues |
Eigenvector matrix based on the affinity matrix |
eigenvectorLengthLessOne |
the eigenvector matrix size minus 1 |
clusterNumber |
the cluster position number being tested |
reverseClusterNumber |
the number of the inverse position of the cluster being tested |
Details
Gets the product of the value and its opposite in relation to the matrix of the eigenvectors
Value
Product of the cluster position in relation to the matrix of eigenvectors
Author(s)
Luis Gustavo Uzai
Clustering with the smallest eigenvectors from eigenvector Matrix
Description
Clustering with the smallest eigenvectors from eigenvector Matrix
Usage
getSpectralClusters(eigenvectorMatrix, numberOfClusters = 2)
Arguments
eigenvectorMatrix |
Eigenvector matrix based on the affinity matrix |
numberOfClusters |
maximum number of clusters for prediction |
Details
Modified standard function present in kernlab to perform clustering with graph spectrum using standard version of K-Means
Value
K-Means Cluster Object
Author(s)
Luis Gustavo Uzai