Distance Matrix
This KNIME component calculates the distance matrix for a given dataset using the scipy.spatial.distance library in Python. The component allows you to choose from a variety of distance metrics to compute the pairwise distances between data points. The only configurable parameter is the distance metric, which can be set to one of the following options:
braycurtis: Computes the Bray-Curtis distance.
canberra: Computes the Canberra distance.
chebyshev: Computes the Chebyshev distance.
cityblock: Computes the City Block (Manhattan) distance.
correlation: Computes the Correlation distance.
cosine: Computes the Cosine distance.
euclidean: Computes the Euclidean distance.
jensenshannon: Computes the Jensen-Shannon distance.
mahalanobis: Computes the Mahalanobis distance.
minkowski: Computes the Minkowski distance.
seuclidean: Computes the Standardized Euclidean distance.
sqeuclidean: Computes the Squared Euclidean distance.
dice: Computes the Dice distance.
hamming: Computes the Hamming distance.
jaccard: Computes the Jaccard distance.
kulczynski1: Computes the Kulczynski distance.
rogerstanimoto: Computes the Rogers-Tanimoto distance.
russellrao: Computes the Russell-Rao distance.
sokalmichener: Computes the Sokal-Michener distance.
sokalsneath: Computes the Sokal-Sneath distance.
yule: Computes the Yule distance.
Usage:
Input your dataset into the component.
Select the desired distance metric from the configuration options.
Execute the component to obtain the distance matrix.
This component is useful for various applications such as clustering, multidimensional scaling, and other analyses that require distance computations.
Version: 1.0
Created by: Carlos Enrique Diaz, MBM, B.Eng.
Email: carlos.diaz@usask.ca