{clustering} R# Documentation

clustering

require(R);

#' R# data clustering tools
imports "clustering" from "MLkit";

R# data clustering tools

R# data clustering tools



.NET clr function exports
gmm

Construct a Gaussian Mixture Model with specific n components
gmm.predict

Get cluster assign result
gmm.components
gmm.predict_proba
cmeans

the cmeans algorithm module

Fuzzy clustering (also referred to as soft clustering) is a form of clustering in which each data point can belong to more than one cluster.

Clustering Or cluster analysis involves assigning data points to clusters (also called buckets, bins, Or classes), Or homogeneous classes, such that items in the same class Or cluster are as similar as possible, while items belonging to different classes are as dissimilar as possible. Clusters are identified via similarity measures. These similarity measures include distance, connectivity, And intensity. Different similarity measures may be chosen based on the data Or the application. >https://en.wikipedia.org/wiki/Fuzzy_clustering
getTraceback

get the clustering traceback
canopy

auto detects k centers for k-means and generates the initial center seeds.
affinity_propagation
kmeans

K-Means Clustering
lloyds
silhouette_score

Silhouette Coefficient
calinski_harabasz
hclust

Hierarchical Clustering Hierarchical cluster analysis on a set of dissimilarities and methods for analyzing it.
btree

do clustering via binary tree
density

evaluate density of the raw data
clusters

get or set the cluster class labels
cluster.groups

get cluster result data
dbscan_objects

find objects from a given set of 2d points
hdbscan
knn

K-NN Classifier in R Programming

K-Nearest Neighbor or K-NN is a Supervised Non-linear classification algorithm. K-NN is a Non-parametric algorithm i.e it doesn’t make any assumption about underlying data or its distribution. It is one of the simplest and widely used algorithm which depends on it’s k value (Neighbors) and finds it’s applications in many industries like finance industry, healthcare industry etc.
dbscan

DBSCAN density reachability and connectivity clustering

Generates a density based clustering of arbitrary shape as introduced in Ester et al. (1996). Clusters require a minimum no of points (MinPts) within a maximum distance (eps) around one of its members (the seed). Any point within eps around any point which satisfies the seed condition is a cluster member (recursively). Some points may not belong to any clusters (noise).
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