| Type: | Package |
| Title: | Educational Outlier Package with Common Outlier Detection Algorithms |
| Version: | 1.0.0 |
| Author: | Andres Missiego Manjon [aut, cre], Juan Jose Cuadrado Gallego [aut] |
| Maintainer: | Andres Missiego Manjon <andres.missiego@edu.uah.es> |
| Description: | Provides implementations of some of the most important outlier detection algorithms. Includes a tutorial mode option that shows a description of each algorithm and provides a step-by-step execution explanation of how it identifies outliers from the given data with the specified input parameters. References include the works of Azzedine Boukerche, Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020) <doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011) <doi:10.1002/widm.19>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.1 |
| VignetteBuilder: | knitr |
| Suggests: | knitr, rmarkdown |
| NeedsCompilation: | no |
| Packaged: | 2024-06-05 08:13:41 UTC; missi |
| Repository: | CRAN |
| Date/Publication: | 2024-06-05 20:00:02 UTC |
DBSCAN_method
Description
Outlier detection method using DBSCAN
Usage
DBSCAN_method(inputData, max_distance_threshold, min_pts, tutorialMode)
Arguments
inputData |
Input Data (must be a data.frame) |
max_distance_threshold |
This is used to calculate the distance between all the points and check if the euclidean distance is less than the max_distance_threshold parameter to decide if add it to the neighbors or not |
min_pts |
the minimum number of points to form a dense region |
tutorialMode |
if TRUE the tutorial mode is activated (the algorithm will include an explanation detailing the theory behind the outlier detection algorithm and a step by step explanation of how is the data processed to obtain the outliers following the theory mentioned earlier) |
Value
None, does not return any value
Author(s)
Andres Missiego Manjon
Examples
inputData = t(matrix(c(3,2,3.5,12,4.7,4.1,5.2,
4.9,7.1,6.1,6.2,5.2,14,5.3),2,7,dimnames=list(c("r","d"))));
inputData = data.frame(inputData);
eps = 4;
min_pts = 3;
DBSCAN_method(inputData, eps, min_pts, FALSE); #Can be set to TRUE
Box And Whiskers
Description
This function implements the box & whiskers algorithm to detect outliers
Usage
boxandwhiskers(data, d, tutorialMode)
Arguments
data |
Input data. |
d |
Degree of outlier or distance at which an event is considered an outlier |
tutorialMode |
if TRUE the tutorial mode is activated (the algorithm will include an explanation detailing the theory behind the outlier detection algorithm and a step by step explanation of how is the data processed to obtain the outliers following the theory mentioned earlier) |
Value
None, does not return any value
Author(s)
Andres Missiego Manjon
Examples
inputData = t(matrix(c(3,2,3.5,12,4.7,4.1,5.2,
4.9,7.1,6.1,6.2,5.2,14,5.3),2,7,dimnames=list(c("r","d"))))
inputData = data.frame(inputData)
boxandwhiskers(inputData,2,FALSE) # Can be set to TRUE
euclidean_distance
Description
This function calculates the euclidean distance between 2 points. They must have the same number of dimensions
Usage
euclidean_distance(p1, p2)
Arguments
p1 |
One of the points that will be used by the algorithm with N dimensions |
p2 |
The other point that will be used by the algorithm with N dimensions |
Value
Euclidean Distance calculated between the two N-dimensional points
Author(s)
Andres Missiego Manjon
Examples
inputData = t(matrix(c(3,2,3.5,12,4.7,4.1,5.2,
4.9,7.1,6.1,6.2,5.2,14,5.3),2,7,dimnames=list(c("r","d"))));
inputData = data.frame(inputData);
point1 = inputData[1,];
point2 = inputData[4,];
distance = euclidean_distance(point1, point2);
knn
Description
This function implements the knn algorithm for outlier detection
Usage
knn(data, d, K, tutorialMode)
Arguments
data |
Input Data (must be a data.frame) |
d |
Degree of outlier or distance at which an event is considered an outlier |
K |
Nearest neighbor for which an event must have a degree of outlier to be considered an outlier |
tutorialMode |
if TRUE the tutorial mode is activated (the algorithm will include an explanation detailing the theory behind the outlier detection algorithm and a step by step explanation of how is the data processed to obtain the outliers following the theory mentioned earlier) |
Value
None, does not return any value
Author(s)
Andres Missiego Manjon
Examples
inputData = t(matrix(c(3,2,3.5,12,4.7,4.1,5.2,
4.9,7.1,6.1,6.2,5.2,14,5.3),2,7,dimnames=list(c("r","d"))))
inputData = data.frame(inputData)
knn(inputData,3,2,FALSE) #Can be changed to TRUE
lof
Description
Local Outlier Factor algorithm to detect outliers
Usage
lof(inputData, K, threshold, tutorialMode)
Arguments
inputData |
Input Data (must be a data.frame) |
K |
This number represents the nearest neighbor to use to calculate the density of each point. This value is chosen arbitrarily and is responsibility of the data scientist/user to select a number adequate to the dataset. |
threshold |
Value that is used to classify the points comparing it to the calculated ARDs of the points in the dataset. If the ARD is smaller, the point is classified as an outliers. If not, the point is classified as a normal point (inlier) |
tutorialMode |
if TRUE the tutorial mode is activated (the algorithm will include an explanation detailing the theory behind the outlier detection algorithm and a step by step explanation of how is the data processed to obtain the outliers following the theory mentioned earlier) |
Value
None, does not return any value
Author(s)
Andres Missiego Manjon
Examples
inputData = t(matrix(c(3,2,3.5,12,4.7,4.1,5.2,
4.9,7.1,6.1,6.2,5.2,14,5.3),2,7,dimnames=list(c("r","d"))));
inputData = data.frame(inputData);
lof(inputData,3,0.5,FALSE) #Can be changed to TRUE
mahalanobis_distance
Description
Calculates the mahalanobis_distance given the input data
Usage
mahalanobis_distance(value, sample_mean, sample_covariance_matrix)
Arguments
value |
Point to calculate the mahalanobis_distance |
sample_mean |
Sample mean |
sample_covariance_matrix |
Sample Covariance Matrix |
Value
Mahalanobis distance associated to the point
Author(s)
Andres Missiego Manjon
Examples
inputData = t(matrix(c(3,2,3.5,12,4.7,4.1,5.2,
4.9,7.1,6.1,6.2,5.2,14,5.3),2,7,dimnames=list(c("r","d"))));
inputData = data.frame(inputData);
inputData = as.matrix(inputData);
sampleMeans = c();
for(i in 1:ncol(inputData)){
column = inputData[,i];
calculatedMean = sum(column)/length(column);
print(sprintf("Calculated mean for column %d: %f", i, calculatedMean))
sampleMeans = c(sampleMeans, calculatedMean);
}
covariance_matrix = cov(inputData);
distance = mahalanobis_distance(inputData[3,], sampleMeans, covariance_matrix);
mahalanobis_method
Description
Detect outliers using the Mahalanobis Distance method
Usage
mahalanobis_method(inputData, alpha, tutorialMode)
Arguments
inputData |
Input Data dataset that will be processed (with or not the step by step explanation) to obtain the underlying outliers. It must be a data.frame type. |
alpha |
Significance level alpha. This value indicates the proportion that it is expected to be outliers out of the dataset. It has to be in the range from 0 to 1 |
tutorialMode |
if TRUE the tutorial mode is activated (the algorithm will include an explanation detailing the theory behind the outlier detection algorithm and a step by step explanation of how is the data processed to obtain the outliers following the theory mentioned earlier) |
Value
None, does not return any value
Author(s)
Andres Missiego Manjon
Examples
inputData = t(matrix(c(3,2,3.5,12,4.7,4.1,5.2,
4.9,7.1,6.1,6.2,5.2,14,5.3),2,7,dimnames=list(c("r","d"))));
inputData = data.frame(inputData);
mahalanobis_method(inputData, 0.7, FALSE); #Can be set to TRUE
manhattan_dist
Description
Calculates the manhattan distance between two 2D points
Usage
manhattan_dist(A, B)
Arguments
A |
One of the 2D points |
B |
The other 2D point |
Value
Manhattan distance calculated between point A and B
Author(s)
Andres Missiego Manjon
Examples
distance = manhattan_dist(c(1,2), c(3,4));
mean_outliersLearn
Description
Calculates the mean of the given data vector
Usage
mean_outliersLearn(data)
Arguments
data |
Input Data that will be processed to calculate the mean. It must be a vector |
Value
Mean of the input data
Author(s)
Andres Missiego Manjon
Examples
mean = mean_outliersLearn(c(2,3,2.3,7.8));
quantile_outliersLearn
Description
Function that obtains the 'v' quantile
Usage
quantile_outliersLearn(data, v)
Arguments
data |
Input Data |
v |
Goes from 0 to 1 (e.g. 0.25). Indicates the quantile that wants to be obtained |
Value
Quantile v calculated
Author(s)
Andres Missiego Manjon
Examples
q = quantile_outliersLearn(c(12,2,3,4,1,13), 0.60)
sd_outliersLearn
Description
Calculates the standard deviation of the input data given the mean.
Usage
sd_outliersLearn(data, mean)
Arguments
data |
Input Data that will be used to calculate the standard deviation. Must be a vector |
mean |
Mean of the input data vector of the function. |
Value
Standard Deviation of the input data
Author(s)
Andres Missiego Manjon
Examples
inputData = c(1,2,3,4,5,6,1);
mean = sum(inputData)/length(inputData);
sd = sd_outliersLearn(inputData, mean);
transform_to_vector
Description
Transform any type of data to a vector
Usage
transform_to_vector(data)
Arguments
data |
Input data that will be transformed into a vector |
Value
Data formatted as a vector
Author(s)
Andres Missiego Manjon
Examples
numeric_data = c(1, 2, 3)
character_data = c("a", "b", "c")
logical_data = c(TRUE, FALSE, TRUE)
factor_data = factor(c("A", "B", "A"))
integer_data = as.integer(c(1, 2, 3))
complex_data = complex(real = c(1, 2, 3), imaginary = c(4, 5, 6))
list_data = list(1, "apple", TRUE)
data_frame_data = data.frame(x = c(1, 2, 3), y = c("a", "b", "c"))
transformed_numeric = transform_to_vector(numeric_data)
transformed_character = transform_to_vector(character_data)
transformed_logical = transform_to_vector(logical_data)
transformed_factor = transform_to_vector(factor_data)
transformed_integer = transform_to_vector(integer_data)
transformed_complex = transform_to_vector(complex_data)
transformed_list = transform_to_vector(list_data)
transformed_data_frame = transform_to_vector(data_frame_data)
z_score_method
Description
This function implements the outlier detection algorithm using standard deviation and mean
Usage
z_score_method(data, d, tutorialMode)
Arguments
data |
Input Data that will be processed with or without the tutorial mode activated |
d |
Degree of outlier or distance at which an event is considered an outlier |
tutorialMode |
if TRUE the tutorial mode is activated (the algorithm will include an explanation detailing the theory behind the outlier detection algorithm and a step by step explanation of how is the data processed to obtain the outliers following the theory mentioned earlier) |
Value
None, does not return any value
Author(s)
Andres Missiego Manjon
Examples
inputData = t(matrix(c(3,2,3.5,12,4.7,4.1,5.2,
4.9,7.1,6.1,6.2,5.2,14,5.3),2,7,dimnames=list(c("r","d"))))
inputData = data.frame(inputData)
z_score_method(inputData,2,FALSE) #Can be changed to TRUE