Package elki.clustering.correlation
Class ERiC.Settings
- java.lang.Object
-
- elki.clustering.correlation.ERiC.Settings
-
- Enclosing class:
- ERiC
public static class ERiC.Settings extends java.lang.Object
Class to wrap the ERiC settings.- Author:
- Erich Schubert
-
-
Field Summary
Fields Modifier and Type Field Description double
delta
Parameter to specify the threshold for approximate linear dependency: the strong eigenvectors of q are approximately linear dependent from the strong eigenvectors p if the following condition holds for all strong eigenvectors q_i of q (lambda_q < lambda_p): q_i' * M^check_p * q_i <= delta^2, must be a double equal to or greater than 0.EigenPairFilter
filter
Filter for Eigenvectors.int
k
Neighborhood size.int
minpts
Minimum neighborhood size (density).PCARunner
pca
Class to compute PCA.double
tau
Parameter to specify the threshold for the maximum distance between two approximately linear dependent subspaces of two objects p and q (lambda_q < lambda_p) before considering them as parallel, must be a double equal to or greater than 0.
-
Constructor Summary
Constructors Constructor Description Settings()
-
-
-
Field Detail
-
k
public int k
Neighborhood size.
-
pca
public PCARunner pca
Class to compute PCA.
-
filter
public EigenPairFilter filter
Filter for Eigenvectors.
-
delta
public double delta
Parameter to specify the threshold for approximate linear dependency: the strong eigenvectors of q are approximately linear dependent from the strong eigenvectors p if the following condition holds for all strong eigenvectors q_i of q (lambda_q < lambda_p): q_i' * M^check_p * q_i <= delta^2, must be a double equal to or greater than 0.
-
tau
public double tau
Parameter to specify the threshold for the maximum distance between two approximately linear dependent subspaces of two objects p and q (lambda_q < lambda_p) before considering them as parallel, must be a double equal to or greater than 0.
-
minpts
public int minpts
Minimum neighborhood size (density).
-
-