CKNN.Construction {HGC} | R Documentation

## Building Unweighted Continuous K Nearest Neighbor Graph

### Description

This function builds a Continuous K Nearest Neighbor (CKNN) graph in the input feature space using Euclidean distance metric.

### Usage

    CKNN.Construction(mat, k, delta)

### Arguments

`mat` | the input data saved as a numerical matrix. The columns are the features and the rows are the samples.
`k` | the number of nearest neighbors for building the CKNN graph.
`delta` | the parameter related with the distance threshold.

### Details

This function fist built a KNN graph from the input data. Then the CKNN graph is built from the KNN graph. For node i and node j in the KNN graph, CKNN will link them if the distance d(i,j) between node i and node j is less than δ times of the geometric mean of d_k(i) and d_k(j). Here δ is the parameter, d_k(i) and d_k(j) are the distances from node i or node j to their k nearest neighbor.

### Value

An n by n binary dgCMatrix object C, where n is the number of input samples. The matrix C is the adjacency matrix of the built CKNN graph. C[i,j] = 1 means that there is an edge between sample i and sample j.

### Examples

    data(Pollen)
    Pollen.PCs <- Pollen[["PCs"]]
    G <- CKNN.Construction(Pollen.PCs)

[Package HGC version 1.2.0 Index]