DCEM (version 1.0.0)

dcem_train: dcem_train: Part of DCEM package.

Description

Learn the Gaussian parameters, mean and co-variance from the dataset. It calls the relevant clustering routine internally dcem_cluster_uv (univariate data) and dcem_cluster_mv (multivariate data).

Usage

dcem_train(data, threshold, iteration_count,  num_clusters, seeding)

Arguments

data

(dataframe): The dataframe containing the data. See trim_data for cleaning the data.

threshold

(decimal): A value to check for convergence (if the means are within this value then the algorithm stops and exit). Default: 0.00001.

iteration_count

(numeric): The number of iterations for which the algorithm should run, if the convergence is not achieved within the specified count then the algorithm stops and exit. Default: 200.

num_clusters

(numeric): The number of clusters. Default: 2

seeding

(string): The initialization scheme ('rand', 'improved'). Default: rand

Value

A list of objects. This list contains parameters associated with the Gaussian(s) (posterior probabilities, mean, standard-deviation and priors). The parameters can be accessed as follows where sample_out is the list containing the output:

1. (1) Posterior Probabilities: sample_out$prob A matrix of posterior-probabilities

2. (2) Mean(s): sample_out$mean

   For multivariate data: It is a matrix of means for the Gaussian(s). Each row in the matrix corresponds to a mean for the Gaussian.

   For univariate data: It is a vector of means. Each element of the vector corresponds to one Gaussian.

3. (3) Co-variance matrices: sample_out$cov

   For multivariate data: List of co-variance matrices for the Gaussian(s).

   Standard-deviation: sample_out$sd

   For univariate data: Vector of standard deviation for the Gaussian(s))

4. (4) Priors: sample_out$prior A vector of priors for the Gaussian(s).

References

Using data to build a better EM: EM* for big data.

Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <https://doi.org/10.1007/s41060-017-0062-1>.

Examples

# NOT RUN {
# Simulating a mixture of univariate samples from three distributions
# with mean as 20, 70 and 100 and standard deviation as 10, 100 and 40 respectively.
sample_uv_data = as.data.frame(c(rnorm(100, 20, 10), rnorm(70, 70, 100), rnorm(50, 100, 40)))

# Randomly shuffle the samples.
sample_uv_data = as.data.frame(sample_uv_data[sample(nrow(sample_uv_data)),])

# Calling the dcem_train() function on the simulated data with threshold of
# 0.000001, iteration count of 1000 and random seeding respectively.
sample_uv_out = dcem_train(sample_uv_data, num_clusters = 3, iteration_count = 100,
threshold = 0.001)

# Simulating a mixture of multivariate samples from 2 gaussian distributions.
sample_mv_data = as.data.frame(rbind(MASS::mvrnorm(n=100, rep(2,5), Sigma = diag(5)),
MASS::mvrnorm(n=50, rep(14,5), Sigma = diag(5))))

# Calling the dcem_train() function on the simulated data with threshold of
# 0.00001, iteration count of 100 and random seeding method respectively.
sample_mv_out = dcem_train(sample_mv_data, threshold = 0.001, iteration_count = 100)

sample_mv_out$mean
#[1,]  2.053163  2.023351  2.017288  1.999596  1.983142
#[2,] 13.948244 14.010651 13.897140 14.285898 13.752592

# }