Package 'kkmeans'

Classify new data based on kkmeans result

Description

Given training data, test data, and kkmeans-results, get which partitions a new set of data belong to

Usage

cluster_new(test_data, train_data, train_result)

Arguments

test_data	the new data to be classified
train_data	the data to make classifications from
train_result	kkmeans result containing the result from classifying train_data

Value

A vector of class labels for test_data corresponding to the clusters present in train_result.

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Get the kernel matrix for a dataset

Description

Given a dataset, kernel function, and tuning parameter, will return the n x n kernel matrix

Usage

get_kernel_matrix(data, kern = "g", param1 = 1, param2 = 1)

Arguments

data	data vector
kern	the kernel to use, one of ('gaussian', 'poly', 'sigmoid', 'laplacian'), can use first letter
param1	first parameter to pass to kernel function.
param2	second parameter to pass to kernel function.

Value

An n x n matrix for data given by the specified kernel. The value in position (i, j) corresponds to the kernel function evaluated at data[i, ] and data[j, ].

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Get the average distance to each points k-nearest neighbor

Description

Given a dataset and a value k, will return the value of the average distance from each point to it's k-nearest neighbor

Usage

get_mknn_dist(data, k = FALSE)

Arguments

data	the data vector
k	which neighbor to average over

Value

The average distance from each point to it's k-th nearest neighbor.

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Function to get jump statistic for varying values of k

Description

Obtains the jump statistic for a particular kernel for the specified number of clusters

Usage

jump_stat(data, kern = "g", param = 1, k_max, eta, iter_max = 1000L)

Arguments

data	Numeric data to cluster. This will be converted to a matrix using as.matrix.
kern	The kernel to use.
param	The parameter value to pass to the kernel
k_max	The maximum number of clusters to consider
eta	Power for the jump statistic
iter_max	Maximum number of iterations to use in kkmeans call

Value

Sum of squares and value of jump statistic for 1, ..., K chosen clusters

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An Efficient Kernel K-Means Algorithm

Description

Performs kernel k-means with the specified kernel using an optimal-transfer quick-transfer algorithm.

Usage

kkmeans(
  data,
  k,
  kern = "g",
  param = 1,
  param2 = 1,
  nstart = 10,
  iter_max = 1000L,
  estimate = FALSE,
  nn = 0,
  init_centers = sample(1:k, size = nrow(data), replace = TRUE),
  method = c("otqt", "macqueen", "lloyd", "ot"),
  trueest = FALSE,
  kmat = NULL,
  random_centers = TRUE
)

Arguments

data	Numeric data to cluster. This will be converted to a matrix using as.matrix.
k	Number of clusters.
kern	Kernel to use, one of ('gaussian', 'poly', 'sigmoid', 'laplacian').
param	value of parameter to pass to kernel function.(eg sigma in gaussian kernel). The Gaussian kernel is K(x, y) = exp(- ||x - y||^2 / (2*param))), and the polynomial kernel is K(x, y) = (x'y + a) ^ param
param2	value of second parameter parameter to pass to the kernel function, which correspond to the offset for the sigmoid and polynomial kernels.
nstart	Number of times to run the algorithm. The run with the lowest total within cluster SSE (in feature space) will be returned
iter_max	The maximum number of iterations to allow.
estimate	If using the Gaussian kernel, specifying estimate = "mknn" will use an nn-nearest neighbor method for estimating param.
nn	How many neighbors to consider for mknn estimation.
init_centers	The initial values for cluster membership. If nstart is greater than 1, any start beyond the first iteration will use randomized centers.
method	Which method to use for kernel k-means iteration. One of ("otqt", "macqueen", "lloyd"). "otqt" is a method using optimal-transfer and quick-transfer heuristics similar to the Hartigan and Wong algorithm for k-means clustering.
trueest	Whether or not the within-cluster sum of squares should be recomputed in R after clustering is finished
kmat	kernel matrix, if using a custom kernel
random_centers	if TRUE, then assign k observations as initial clusters, assigning the remaining observations to the closest cluster. Otherwise, assign all observations to clusters at random.

Value

A list containing the following useful information

cluster

The final cluster membership.

centers

A k x p matrix, the rows of which contain the centers of the clusters in R^n (not to be confused with the clusters in feature space)

wss

The within-cluster sum of squares for each cluster in feature space.

param

The parameter value used.

Examples

data <- as.matrix(iris[, 1:4])

# cluster using linear kernel (normal k-means)
result <- kkmeans(data, k = 3, kern = "poly", param = 1)

# cluster using gaussian kernel
# estimating the parameter with 3-nearest neighbors
result <- kkmeans(data, k = 3, kern = "g", estimate = "mknn", nn = 3)

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Estimate the bandwidth parameter for a gaussian kernel using MATr

Description

given data and number of clusters K, choose a bandwidth from a grid according to the MATR algorithm

Usage

matr(dat, k, grid, tol = 0.01)

Arguments

dat	data vector
k	number of clusters
grid	parameter grid to search on
tol	tolerence for choosing the "best" sigma values. Reducing the tolerance will give larger values of the bandwidth parameter

Value

A list with two elements. The first, l, contains the trace value for each of the values in grid. The second, best, contains the best value.

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