Filter metrics in a clustering object returning a new clustering object.

Description

Generates a new filtered clustering object.

Usage

## S3 method for class 'clustering'
clustering[condition = TRUE]

Arguments

Details

This function allows you to filter the data set for a given evaluation metric. The evaluation metrics available are: Algorithm, Distance, Clusters, Data, Var, Time, Entropy, Variation_information, Precision, Recall, F_measure, Fowlkes_mallows_index, Connectivity, Dunn, Silhouette and TimeAtt.

Value

A clustering object filtered from the input parameters.

Examples

result <- Clustering::clustering(df = Clustering::basketball, algorithm = 'clara',
min=3, max=4, metrics = c('Precision','Recall'))

result[Precision > 0.14 & Recall > 0.11]

Method that runs the aggExcluster algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the aggExcluster algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

aggExCluster_euclidean(dt, clusters, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the agnes algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the agnes algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

agnes_euclidean_method(dt, clusters, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the agnes algorithm using the manhattan metric to make an external or internal validation of the cluster

Description

Method that runs the agnes algorithm using the manhattan metric to make an external or internal validation of the cluster

Usage

agnes_manhattan_method(dt, clusters, metric)

Arguments

Value

returns a list with both the internal and external evaluation of the grouping.

amap package algorithms

Description

amap package algorithms

Usage

algorithm_amap()

Value

list with the algorithms

apcluster package algorithms

Description

apcluster package algorithms

Usage

algorithm_apcluster()

Value

list with the algorithms

cluster package algorithms

Description

cluster package algorithms

Usage

algorithm_cluster()

Value

list with the algorithms

ClusterR package algorithms

Description

ClusterR package algorithms

Usage

algorithm_clusterr()

Value

list with the algorithms

pvclust package algorithms

Description

pvclust package algorithms

Usage

algorithm_pvclust()

Value

list with the algorithms

Method that returns the list of used algorithms

Description

Method that returns the list of used algorithms

Usage

algorithms()

Value

algorithm listing array

Method that returns all the algorithms executed by the package

Description

Method that returns all the algorithms executed by the package

Usage

algorithms_package(packages)

Arguments

Value

array with the algorithms we're going to run

Method that runs the apClusterK algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the apClusterK algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

apclusterK_euclidean(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the apclusterK algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Description

Method that runs the apclusterK algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Usage

apclusterK_manhattan(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the apclusterK algorithm using the Minkowski metric to make an external or internal validation of the cluster.

Description

Method that runs the apclusterK algorithm using the Minkowski metric to make an external or internal validation of the cluster.

Usage

apclusterK_minkowski(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Clustering GUI.

Description

Method that allows us to execute the main algorithm in graphic interface mode instead of through the console.

Usage

appClustering()

Details

The operation of this method is to generate a graphical user. interface to be able to execute the clustering algorithm without knowing the parameters. Its operation is very simple, we can change the values and see the behavior quickly.

Value

GUI with the parameters of the algorithm and their representation in tables and graphs.

This data set contains a series of statistics (5 attributes) about 96 basketball players:

Description

This data set contains a series of statistics about basketball players:

Usage

data(basketball)

Format

A data frame with 96 observations on 5 variables:

This data set contains a series of statistics about basketball players:

assists_per_minuteReal

average number of assistances per minute

heightInteger

height of the player

time_playedReal

time played by the player

ageInteger

number of years of the player

points_per_minuteReal

average number of points per minute

Source

KEEL, <http://www.keel.es/>

Best rated external metrics.

Description

Method in charge of searching for each algorithm those that have the best external classification.

Method that looks for those external attribute that are better classified, making use of the var column. In this way of discard attribute and only work with those that give the best response to the algorithm in question.

Usage

best_ranked_external_metrics(df)

Arguments

Value

Returns a data.frame with the best classified external attribute.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 4,
               algorithm='clara',
               metrics=c("Recall")
         )

Clustering::best_ranked_external_metrics(df = result)

Best rated internal metrics.

Description

Method in charge of searching for each algorithm those that have the best internal classification.

Method that looks for those internal attributes that are better classified, making use of the Var column. In this way we discard the attributes and only work with those that give the best response to the algorithm in question.

Usage

best_ranked_internal_metrics(df)

Arguments

Value

Returns a data.frame with the best classified internal attributes.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='gmm',
               metrics=c("Recall")
         )

Clustering::best_ranked_internal_metrics(df = result)

Data from an experiment on the affects of machine adjustments on the time to count bolts.

Description

A manufacturer of automotive accessories provides hardware, e.g. nuts, bolts, washers and screws, to fasten the accessory to the car or truck. Hardware is counted and packaged automatically. Specifically, bolts are dumped into a large metal dish. A plate that forms the bottom of the dish rotates counterclockwise. This rotation forces bolts to the outside of the dish and up along a narrow ledge. Due to the vibration of the dish caused by the spinning bottom plate, some bolts fall off the ledge and back into the dish. The ledge spirals up to a point where the bolts are allowed to drop into a pan on a conveyor belt. As a bolt drops, it passes by an electronic eye that counts it. When the electronic counter reaches the preset number of bolts, the rotation is stopped and the conveyor belt is moved forward

Usage

data(bolts)

Format

A data frame with 40 observations on 8 variables:

A manufacturer of automotive accessories provides hardware, e.g. nuts, bolts, washers and screws, to fasten the accessory to the car or truck. Hardware is counted and packaged automatically. Specifically, bolts are dumped into a large metal dish. A plate that forms the bottom of the dish rotates counterclockwise. This rotation forces bolts to the outside of the dish and up along a narrow ledge. Due to the vibration of the dish caused by the spinning bottom plate, some bolts fall off the ledge and back into the dish. The ledge spirals up to a point where the bolts are allowed to drop into a pan on a conveyor belt. As a bolt drops, it passes by an electronic eye that counts it. When the electronic counter reaches the preset number of bolts, the rotation is stopped and the conveyor belt is moved forward

RUNInteger

is the order in which the data were collected

SPEED1Integer

a speed setting that controls the speed of rotation of the plate at the bottom of the dish

TOTALInteger

total number of bolts (TOTAL) to be counted

SPEED2Integer

a second speed setting hat is used to change the speed of rotation (usually slowing it down) for the last few bolts

NUMBER2Integer

the number of bolts to be counted at this second speed

SENSInteger

the sensitivity of the electronic eye

TIMEReal

The measured response is the time, in seconds

T20BOLTReal

n order to put times on a equal footing the response to be analyzed is the time to count 20 bolts

Details

There are several adjustments on the machine that affect its operation. These include; a speed setting that controls the speed of rotation (SPEED1Integer) of the plate at the bottom of the dish, a total number of bolts (TOTAL) to be counted, a second speed setting (SPEED2Integer) that is used to change the speed of rotation (usually slowing it down) for the last few bolts, the number of bolts to be counted at this second speed (NUMBER2Integer), and the sensitivity of the electronic eye (SENSInteger). The sensitivity setting is to insure that the correct number of bolts are counted. Too few bolts packaged causes customer complaints. Too many bolts packaged increases costs. For each run conducted in this experiment the correct number of bolts was counted. From an engineering standpoint if the correct number of bolts is counted, the sensitivity should not affect the time to count bolts. The measured response is the time (TIMEReal), in seconds, it takes to count the desired number of bolts. In order to put times on a equal footing the response to be analyzed is the time to count 20 bolts (T20BOLTReal). Below are the data for 40 combinations of settings. RUNinteger is the order in which the data were collected.

Source

KEEL, <http://www.keel.es/>

Method that calculates the best rated external metrics.

Description

Method that calculates the best rated external metrics.

Usage

calculate_best_external_variables_by_metrics(df)

Arguments

Value

Return a table with the external metrics that has the best rating.

Method that calculates the best rated internal metrics.

Description

Method that calculates the best rated internal metrics.

Usage

calculate_best_internal_variables_by_metrics(df)

Arguments

Value

Return a table with the internal metrics that has the best rating.

Method that calculates which algorithm and which metric behaves best for the datasets provided.

Description

Method that calculates which algorithm and which metric behaves best for the datasets provided.

Usage

calculate_best_validation_external_by_metrics(df, metric)

Arguments

Value

Return a table with the algorithm and the best performing metric for the datasets.

Method that calculates which algorithm and which metric behaves best for the datasets provided.

Description

Method that calculates which algorithm and which metric behaves best for the datasets provided.

Usage

calculate_best_validation_internal_by_metrics(df, metric)

Arguments

Value

Return a table with the algorithm and the best performing metric for the datasets.

Method to calculate the Connectivity

Description

Method to calculate the Connectivity

Usage

calculate_connectivity(
  distance = NULL,
  clusters,
  datadf = NULL,
  neighbSize = 12,
  method = "euclidean"
)

Arguments

Value

Return a double with the result of the connectivity calculation.

Method to calculate the dunn.

Description

Method to calculate the dunn.

Usage

calculate_dunn(distance = NULL, clusters, datadf = NULL, method = "euclidean")

Arguments

Value

Return a double with the result of the dunn calculation

Method that returns the value or variable depending on where it is in the calculated metrics.

Description

Method that returns the value or variable depending on where it is in the calculated metrics.

Usage

calculate_result(
  algorith,
  distance,
  cluster,
  dataset,
  ranking,
  timeExternal,
  entropy,
  variation_information,
  precision,
  recall,
  fowlkes_mallows_index,
  f_measure,
  timeInternal,
  dunn,
  connectivity,
  silhouette,
  variables
)

Arguments

Value

Returns an array with the calculation of each metric based on the indicated position.

Method that returns the value or variable depending on where it is in the calculated metrics.

Description

Method that returns the value or variable depending on where it is in the calculated metrics.

Usage

calculate_result_internal(
  algorith,
  distance,
  cluster,
  dataset,
  ranking,
  timeInternal,
  dunn,
  connectivity,
  silhouette,
  variables
)

Arguments

Value

Returns an array with the calculation of each metric based on the indicated position.

Method that calculates which algorithm behaves best for the datasets provided.

Description

Method that calculates which algorithm behaves best for the datasets provided.

Usage

calculate_validation_external_by_metrics(df)

Arguments

Value

Return a table with the best performing algorithm for the provided datasets.

Method that calculates which algorithm behaves best for the datasets provided.

Description

Method that calculates which algorithm behaves best for the datasets provided.

Usage

calculate_validation_internal_by_metrics(df)

Arguments

Value

Return a table with the best performing algorithm for the provided datasets.

Method that runs the clara algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the clara algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

clara_euclidean_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the clara algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Description

Method that runs the clara algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Usage

clara_manhattan_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Clustering algorithm.

Description

Discovering the behavior of attributes in a set of clustering packages based on evaluation metrics.

Usage

clustering(
  path = NULL,
  df = NULL,
  packages = NULL,
  algorithm = NULL,
  min = 3,
  max = 4,
  metrics = NULL
)

Arguments

Details

The operation of this algorithm is to evaluate how the attributes of a dataset or a set of datasets behave in different clustering algorithms. To do this, it is necessary to indicate the type of evaluation you want to make on the distribution of the data. To be able to execute the algorithm it is necessary to indicate the number of clusters.

min and max, the algorithms algorithm or packages.

packages that we want to cluster and the metrics metrics.

Value

A matrix with the result of running all the metrics of the algorithms contained in the packages indicated. We also obtain information with the types of metrics, algorithms and packages executed.

• result It is a list with the algorithms, metrics and variables defined in the execution of the algorithm.

• has_internal_metrics Boolean field to indicate if there are internal metrics such as: dunn, silhoutte and connectivity.

• has_external_metrics Boolean field to indicate if there are external metrics such as: precision, recall, f-measure, entropy, variation information and fowlkes-mallows.

• algorithms_execute Character vector with the algorithms executed. These algorithms have been mentioned in the definition of the parameters.

• measures_execute Character vector with the measures executed. These measures have been mentioned in the definition of the parameters.

Examples

Clustering::clustering(
     df = cluster::agriculture,
     min = 3,
     max = 3,
     algorithm='clara',
     metrics=c('Precision')
)

Method to calculate the connectivity.

Description

Method to calculate the connectivity.

Usage

connectivity_metric(distance, clusters_vector, dt, method)

Arguments

Value

Return a double with the result of the connectivity calculation.

Method that converts a matrix into numerical format.

Description

Method that converts a matrix into numerical format.

Usage

convert_numeric_matrix(datas)

Arguments

Value

return a matrix in numeric format.

Method in charge of creating a table from an array with the values of the variable used as a sample and another with the classification of the values.

Description

Method in charge of creating a table from an array with the values of the variable used as a sample and another with the classification of the values.

Usage

convert_table(clusters_vector, column_dataset_label)

Arguments

Value

Return a table with the grouping of both arrays.

Method to convert columns to ordinal.

Description

Method to convert columns to ordinal.

Usage

convert_toOrdinal(df)

Arguments

Value

convert data frame to Ordinal.

Method that runs the daisy algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the daisy algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

daisy_euclidean_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the daisy algorithm using the Gower metric to make an external or internal validation of the cluster.

Description

Method that runs the daisy algorithm using the Gower metric to make an external or internal validation of the cluster.

Usage

daisy_gower_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the daisy algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Description

Method that runs the daisy algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Usage

daisy_manhattan_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method to filter only the external measurement columns

Description

Method to filter only the external measurement columns

Usage

dataframe_by_metrics_evaluation(data, external = TRUE)

Arguments

Value

returns a data frame with the filtered columns.

Method in charge of detecting the limit of a dataset header.

Description

Method in charge of detecting the limit of a dataset header.

Usage

detect_definition_attribute(path)

Arguments

Value

The row where the dataset attributes definition ends

Method that runs the diana algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the diana algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

diana_euclidean_method(dt, clusters, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method to calculate the dunn.

Description

Method to calculate the dunn.

Usage

dunn_metric(dist, clusters_vector, dt, me)

Arguments

Value

Return a double with the result of the dunn calculation.

Method for calculating entropy.

Description

Method for calculating entropy.

Usage

entropy_formula(x_vec)

Arguments

Value

An array with the calculate.

Method to calculate the entropy.

Description

Method to calculate the entropy.

Usage

entropy_metric(conversion_data_frame, table_convert, column_dataset_label)

Arguments

Value

Return a double with the result of the entropy calculation.

Method in charge of calculating the average for all datasets using all the algorithms defined in the application.

Description

Method in charge of calculating the average for all datasets using all the algorithms defined in the application.

Usage

evaluate_all_column_dataset(datas, method, cluster, nameDataset, metrics)

Arguments

Value

A list with result of external and internal validation applying on algorithms.

Evaluates algorithms by measures of dissimilarity based on a metric.

Description

Method that calculates which algorithm and which metric behaves best for the datasets provided.

Usage

evaluate_best_validation_external_by_metrics(df, metric)

Arguments

Details

Method groups the data by algorithm and distance measure, instead of obtaining the best attribute from the data set.

Value

A data.frame with the algorithms classified by measures of dissimilarity.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='kmeans_rcpp',
               metrics=c("F_measure"))

Clustering::evaluate_best_validation_external_by_metrics(result,'F_measure')

Evaluates algorithms by measures of dissimilarity based on a metric.

Description

Method that calculates which algorithm and which metric behaves best for the datasets provided.

Usage

evaluate_best_validation_internal_by_metrics(df, metric)

Arguments

Details

This method groups the data by algorithm and distance measure, instead of obtaining the best attribute from the data set.

Value

A data.frame with the algorithms classified by measures of dissimilarity.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='gmm',
               metrics=c("Precision","Connectivity")
         )

Clustering::evaluate_best_validation_internal_by_metrics(result,"Connectivity")

Evaluate external validations by algorithm.

Description

Method that calculates which algorithm behaves best for the datasets provided.

Usage

evaluate_validation_external_by_metrics(df)

Arguments

Details

It groups the results of the execution by algorithms.

Value

A data.frame with all the algorithms that obtain the best results regardless of the dissimilarity measure used.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 4,
               algorithm='kmeans_arma',
               metrics=c("Precision")
         )

Clustering::evaluate_validation_external_by_metrics(result)

Evaluate internal validations by algorithm.

Description

Method that calculates which algorithm behaves best for the datasets provided.

Usage

evaluate_validation_internal_by_metrics(df)

Arguments

Details

It groups the results of the execution by algorithms.

Value

A data.frame with all the algorithms that obtain the best results regardless of the dissimilarity measure used.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='kmeans_rcpp',
               metrics=c("Recall","Silhouette")
         )

Clustering::evaluate_validation_internal_by_metrics(result)


Clustering::evaluate_validation_internal_by_metrics(result$result)

Evaluation clustering algorithm.

Description

Method of performing information processing

Usage

execute_datasets(
  path,
  df,
  packages,
  algorithm,
  cluster_min,
  cluster_max,
  metrics,
  attributes,
  name_dataframe
)

Arguments

Value

Returns a matrix with the result of running all the metrics of the algorithms contained in the packages we indicated.

Evaluation clustering algorithm.

Description

Method that evaluates clustering algorithm from a file directory or dataframe.

Usage

execute_package_parallel(
  directory_files,
  df,
  algorithms_execute,
  measures_execute,
  cluster_min,
  cluster_max,
  metrics_execute,
  attributes,
  number_algorithms,
  numberClusters,
  numberDataSets,
  is_metric_external,
  is_metric_internal,
  name_dataframe
)

Arguments

Value

Returns a list with the result matrix of evaluating the data from the indicated algorithms, metrics and number of clusters.

Export result of external metrics in latex.

Description

Method that exports the results of external measurements in latex format to a file.

Usage

export_file_external(df, path = NULL)

Arguments

Details

When we work in latex format and we need to create a table to export the results, with this method we can export the results of the clustering algorithm to latex.

Value

A file in Latex format with the results of the external metrics.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='gmm',
               metrics=c("Precision")
         )

Clustering::export_file_external(result)
file.remove("external_data.tex")

Export result of internal metrics in latex.

Description

Method that exports the results of internal measurements in latex format to a file.

Usage

export_file_internal(df, path = NULL)

Arguments

Details

When we work in latex format and we need to create a table to export the results, with this method we can export the results of the clustering algorithm to latex.

Value

A file in Latex format with the results of the internal metrics.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='gmm',
               metrics=c("Recall","Dunn")
         )

Clustering::export_file_internal(result)
file.remove("internal_data.tex")

Method that return the extension of a file

Description

Method that return the extension of a file

Usage

extension_file(path)

Arguments

Value

return the extension of file

Method that applicate differents external metrics about a data frame or matrix, for example precision, recall etc

Description

Method that applicate differents external metrics about a data frame or matrix, for example precision, recall etc

Usage

external_validation(column_dataset_label, clusters_vector, metric = CONST_NULL)

Arguments

Value

Return a list of the external results initialized to zero.

Method that runs the fanny algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the fanny algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

fanny_euclidean_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the fanny algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Description

Method that runs the fanny algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Usage

fanny_manhattan_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that fill vector

Description

Method that fill vector

Usage

fill_cluster_vector(data, appcluster)

Arguments

Value

a vector fill with information

Method to calculate the f_measure.

Description

Method to calculate the f_measure.

Usage

fmeasure_metric(true_positive, false_positive, false_negative)

Arguments

Value

Returns a double with the f_measure calculation.

Method to calculate the fowlkes and mallows.

Description

Method to calculate the fowlkes and mallows.

Usage

fowlkes_mallows_index_metric(true_positive, false_positive, false_negative)

Arguments

Value

Returns a double with the fowlkes_mallows_index calculation.

Method that runs the gmm algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the gmm algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

gmm_euclidean_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the gmm algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Description

Method that runs the gmm algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Usage

gmm_manhattan_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the hcluster algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the hcluster algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

hclust_euclidean(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that returns an array with the external information of the cluster

Description

Method that returns an array with the external information of the cluster

Usage

information_external(metrics, information, size, variables)

Arguments

Value

array with the information from the calculation of the external evaluation of the clustering

Method that returns an array with the internal information of the cluster

Description

Method that returns an array with the internal information of the cluster

Usage

information_internal(metrics, information, size, variables)

Arguments

Value

array with the information from the calculation of the internal evaluation of the clustering

Method that return a list of internal validation initialized to zero.

Description

Method that return a list of internal validation initialized to zero.

Usage

initializeExternalValidation()

Value

A list of all values set to zero.

Method that return a list of external validation initialized to zero.

Description

Method that return a list of external validation initialized to zero.

Usage

initializeInternalValidation()

Value

A list of all values set to zero.

Method that applicate differents internal metrics about a data frame or matrix, for example dunn, connectivity etc.

Description

Method that applicate differents internal metrics about a data frame or matrix, for example dunn, connectivity etc.

Usage

internal_validation(
  distance = NULL,
  clusters_vector,
  dataf = NULL,
  method = CONST_EUCLIDEAN,
  metric = NULL
)

Arguments

Value

Return a list of the internal results initialized to zero.

Method that checks for external metrics

Description

Method that checks for external metrics

Usage

is_External_Metrics(metrics)

Arguments

Value

true if it exists and false otherwise

Method that checks for internal metrics

Description

Method that checks for internal metrics

Usage

is_Internal_Metrics(metrics)

Arguments

Value

true if it exists and false otherwise

Method that runs the kmeans_arma algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the kmeans_arma algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

kmeans_arma_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the kmeans_rcpp algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the kmeans_rcpp algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

kmeans_rcpp_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that return max value of metric.

Description

Method that return max value of metric.

Usage

max_value_metric(df, metric, isExternalMetric)

Arguments

Value

A value with maximum column.

Metrics of the amap algorithm

Description

Metrics of the amap algorithm

Usage

measure_amap()

Value

list with the metrics

Metrics of the apcluster algorithm

Description

Metrics of the apcluster algorithm

Usage

measure_apcluster()

Value

list with the metrics

Method that returns all the measures executed by the package from the indicated algorithms

Description

Method that returns all the measures executed by the package from the indicated algorithms

Usage

measure_calculate(algorithm)

Arguments

Value

array with the measures we're going to run

Metrics of the cluster algorithm

Description

Metrics of the cluster algorithm

Usage

measure_cluster()

Value

list with the metrics

Metrics of the ClusterR algorithm

Description

Metrics of the ClusterR algorithm

Usage

measure_clusterr()

Value

list with the metrics

Method that returns all the measures executed by the package

Description

Method that returns all the measures executed by the package

Usage

measure_package(package)

Arguments

Value

array with the measures we're going to run

Metrics of the pvclust algorithm

Description

Metrics of the pvclust algorithm

Usage

measure_pvclust()

Value

list with the metrics

Method in charge of verifying the implemented metrics

Description

Method in charge of verifying the implemented metrics

Usage

metrics_calculate(metrics, variables, internal, external)

Arguments

Value

list of metrics

Method that returns the list of used external metrics

Description

Method that returns the list of used external metrics

Usage

metrics_external()

Value

external metrics listing array

Method that returns the list of used internal metrics

Description

Method that returns the list of used internal metrics

Usage

metrics_internal()

Value

internal metrics listing array

Method that returns the list of used metrics

Description

Method that returns the list of used metrics

Usage

metrics_validate()

Value

metrics listing array

Method that runs the mini_kmeans algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the mini_kmeans algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

mini_kmeans_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the mona algorithm using external or internal validation of the cluster.

Description

Method that runs the mona algorithm using external or internal validation of the cluster.

Usage

mona_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that returns how many external metrics there are in the array of metrics used in the calculation

Description

Method that returns how many external metrics there are in the array of metrics used in the calculation

Usage

number_columnas_external(metrics)

Arguments

Value

returns the number of occurrences

Method that returns how many internal metrics there are in the array of metrics used in the calculation

Description

Method that returns how many internal metrics there are in the array of metrics used in the calculation

Usage

number_columnas_internal(metrics)

Arguments

Value

returns the number of occurrences

Method that returns the number of variables in a dataset directory

Description

Method that returns the number of variables in a dataset directory

Usage

number_variables_dataset(path)

Arguments

Value

returns the number of variables in a dataset directory

Method that returns the list of used packages

Description

Method that returns the list of used packages

Usage

packages()

Value

package listing array

Method that runs the pam algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the pam algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

pam_euclidean_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the pam algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Description

Method that runs the pam algorithm using the Manhattan metric to make an external or internal validation of the cluster.

Usage

pam_manhattan_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that return a list of files that exists in a directory

Description

Method that return a list of files that exists in a directory

Usage

path_dataset(directory)

Arguments

Value

a vector with the files existing into of a directory

Graphic representation of the evaluation measures.

Description

Graphical representation of the evaluation measures grouped by cluster.

Usage

plot_clustering(df, metric)

Arguments

Details

In certain cases the review or filtering of the data is necessary to select the data, that is why thanks to the graphic representations this task is much easier. Therefore with this method we will be able to filter the data by metrics and see the data in a graphical way.

Value

Generate an image with the distribution of the clusters by metrics.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='gmm',
               metrics=c("Precision")
         )

Clustering::plot_clustering(result,c("Precision"))

Method to calculate the precision.

Description

Method to calculate the precision.

Usage

precision_metric(true_positive, false_positive)

Arguments

Value

Returns a double with the precision calculation.

Method that runs the pvclust algorithm using the Correlation metric to make an external or internal validation of the cluster.

Description

Method that runs the pvclust algorithm using the Correlation metric to make an external or internal validation of the cluster.

Usage

pvclust_correlation_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the pvclust algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Description

Method that runs the pvclust algorithm using the Euclidean metric to make an external or internal validation of the cluster.

Usage

pvclust_euclidean_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that runs the pvpick algorithm using an external or internal validation of the cluster.

Description

Method that runs the pvpick algorithm using an external or internal validation of the cluster.

Usage

pvpick_method(dt, clusters, columnClass, metric)

Arguments

Value

Return a list with both the internal and external evaluation of the grouping.

Method that converts a dataset into a matrix

Description

Method that converts a dataset into a matrix

Usage

read_file(path)

Arguments

Value

returns a matrix whose content is the dataset received as a parameter

Method to calculate the recall.

Description

Method to calculate the recall.

Usage

recall_metric(true_positive, false_negative)

Arguments

Value

Returns a double with the recall calculation.

Method for refactoring the distance measurement name.

Description

Method for refactoring the distance measurement name.

Usage

refactorName(nameMeasure)

Arguments

Value

a string with the refactored measure name

Method for filtering clustering results.

Description

Method for filtering clustering results.

Usage

resultClustering(result)

Arguments

Value

a matrix with the filtered columns.

External results by algorithm.

Description

It is used for obtaining the results of an algorithm indicated as a parameter grouped by number of clusters.

Usage

result_external_algorithm_by_metric(df, metric)

Arguments

Value

A data.frame with the results of the algorithm indicated as parameter.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='gmm',
               metrics=c("Precision")
         )

Clustering::result_external_algorithm_by_metric(result,'Precision')

Internal results by algorithm

Description

It is used for obtaining the results of an algorithm indicated as a parameter grouped by number of clusters.

Usage

result_internal_algorithm_by_metric(df, metric)

Arguments

Value

A data.frame with the results of the algorithm indicated as parameter.

Examples

result = Clustering::clustering(
               df = cluster::agriculture,
               min = 4,
               max = 5,
               algorithm='gmm',
               metrics=c("Recall","Silhouette")
         )

Clustering::result_internal_algorithm_by_metric(result,'Silhouette')

Method in charge of obtaining those metrics that are external from those indicated.

Description

Method in charge of obtaining those metrics that are external from those indicated.

Usage

row_name_df_external(metrics)

Arguments

Value

Return an array with the metrics that are external.

Method in charge of obtaining those metrics that are internal from those indicated.

Description

Method in charge of obtaining those metrics that are internal from those indicated.

Usage

row_name_df_internal(metrics)

Arguments

Value

Return an array with the metrics that are internal.

Method that returns a table with the algorithm and the metric indicated as parameters.

Description

Method that returns a table with the algorithm and the metric indicated as parameters.

Usage

show_result_external_algorithm_by_metric(df, metric)

Arguments

Value

Return a table with the algorithm and the metric indicated as parameter.

Method in charge of obtaining a table with the results of the algorithms grouped by clusters, calculating the maximum value of each external metrics.

Description

Method in charge of obtaining a table with the results of the algorithms grouped by clusters, calculating the maximum value of each external metrics.

Usage

show_result_external_algorithm_group_by_clustering(df)

Arguments

Value

Return a table with the algorithms and the clusters.

Method that returns a table with the algorithm and the metric indicated as parameters.

Description

Method that returns a table with the algorithm and the metric indicated as parameters.

Usage

show_result_internal_algorithm_by_metric(df, metric)

Arguments

Value

Return a table with the algorithm and the metric indicated as parameter.

Method in charge of obtaining a table with the results of the algorithms grouped by clusters, calculating the maximum value of each internal metrics.

Description

Method in charge of obtaining a table with the results of the algorithms grouped by clusters, calculating the maximum value of each internal metrics.

Usage

show_result_internal_algorithm_group_by_clustering(df)

Arguments

Value

Return a table with the algorithms and the clusters.

Method to calculate the silhouette.

Description

Method to calculate the silhouette.

Usage

silhouette_metric(clusters_vector, distance)

Arguments

Value

Return a double with the result of the silhouette calculation.

Returns the clustering result sorted by a set of metrics.

Description

This function receives a clustering object and sorts the columns by parameter. By default it performs sorting by the algorithm field.

Usage

## S3 method for class 'clustering'
sort(x, decreasing = TRUE, ...)

Arguments

Details

The additional argument in "..." is the 'by' argument, which is a array with the name of the evaluation measure to order by. Valid value are: Algorithm, Distance, Clusters, Data, Var, Time, Entropy, Variation_information, Precision, Recall, F_measure, Fowlkes_mallows_index, Connectivity, Dunn, Silhouette, TimeAtt.

Value

Another clustering object with the evaluation measures sorted

Examples

result <-
Clustering::clustering(df = cluster::agriculture,min = 4, max = 4,algorithm='gmm',
metrics='Recall')

sort(result, FALSE, 'Recall')

Method that format a number with four digits

Description

Method that format a number with four digits

Usage

specify_decimal(x, k)

Arguments

Value

a number convert to string with four digits

The data provided are daily stock prices from January 1988 through October 1991, for ten aerospace companies.

Description

The data provided are daily stock prices from January 1988 through October 1991, for ten aerospace companies.

Usage

data(stock)

Format

A data frame with 950 observations on 10 variables:

The data provided are daily stock prices from January 1988 through October 1991, for ten aerospace companies.

Company1

company1 details

Company2

company2 details

Company3

company3 details

Company4

company4 details

Company5

company5 details

Company6

company6 details

Company7

company7 details

Company8

company8 details

Company9

company9 details

Company10

company10 details

Source

KEEL, <http://www.keel.es/>

The study was performed at the 2nd Department of Medicine, 1st Faculty of Medicine of Charles University and Charles University Hospital. The data were transferred to electronic form by the European Centre of Medical Informatics, Statisticsand Epidemiology of Charles University and Academy of Sciences.

Description

The study was performed at the 2nd Department of Medicine, 1st Faculty of Medicine of Charles University and Charles University Hospital. The data were transferred to electronic form by the European Centre of Medical Informatics, Statisticsand Epidemiology of Charles University and Academy of Sciences.

Usage

data(stulong)

Format

A data frame with 1417 observations on 5 variables.

The study was performed at the 2nd Department of Medicine, 1st Faculty of Medicine of Charles University and Charles University Hospital. The data were transferred to electronic form by the European Centre of Medical Informatics, Statisticsand Epidemiology of Charles University and Academy of Sciences.

a1

Height

a2

Weight

a3

Blood pressure I systolic (mm Hg)

a4

Blood pressure I diastolic (mm Hg)

a5

ercentage Cholesterol in mg

Source

KEEL, <http://www.keel.es/>

Method for filtering external columns of a dataset.

Description

Method for filtering external columns of a dataset.

Usage

transform_dataset(df)

Arguments

Value

Dafa frame filtered with the columns of the external measurements.

Exists internal measure

Method for filtering internal columns of a dataset.

Description

Method for filtering internal columns of a dataset.

Usage

transform_dataset_internal(df)

Arguments

Value

dafa frame filtered with the columns of the internal measurements.

Exists internal measure

Method to calculate the variation information.

Description

Method to calculate the variation information.

Usage

variation_information_metric(conversion_data_frame, table_convert)

Arguments

Value

Returns a double with the result of the variation information calculation.

One of the most known testing data sets in machine learning. This data sets describes several situations where the weather is suitable or not to play sports, depending on the current outlook, temperature, humidity and wind.

Description

One of the most known testing data sets in machine learning. This data sets describes several situations where the weather is suitable or not to play sports, depending on the current outlook, temperature, humidity and wind.

Usage

data(weather)

Format

A data frame with 14 observations on 5 variables:

One of the most known testing data sets in machine learning. This data sets describes several situations where the weather is suitable or not to play sports, depending on the current outlook, temperature, humidity and wind.

Outlook

sunny, overcast, rainy

Temperature

hot, mild, cool

Humidity

high, normal

Windy

true, false

Play

yes, no

Source

KEEL, <http://www.keel.es/>