knn_dist

<code>knn_dist</code> searches for nearest neighbors in a dissimilarity matrix matrix.

Various clustering algorithms that produce a credal partition,
i.e., a set of Dempster-Shafer mass functions representing the membership of objects
to clusters. The mass functions quantify the cluster-membership uncertainty of the
objects. The algorithms are: Evidential c-Means, Relational Evidential c-Means,
Constrained Evidential c-Means, Evidential Clustering, Constrained Evidential
Clustering, Evidential K-nearest-neighbor-based Clustering, Bootstrap Model-Based
Evidential Clustering, Belief Peak Evidential Clustering, Neural-Network-based
Evidential Clustering.

Thierry Denoeux

evclust

Evidential Clustering

knn_dist function

<dl><dt>D</dt>
<dd>Dissimilarity matrix of size (n,n), where n is the number of objects.</dd>
<dt>K</dt>
<dd>Number of neighbors</dd></dl>

Arguments

Author

K nearest neighbors in a dissimilarity matrix — knn_dist

<dl>

<dt>D</dt>
<dd>Dissimilarity matrix of size (n,n), where n is the number of objects.</dd>


<dt>K</dt>
<dd>Number of neighbors</dd>

</dl>

knn_dist: K nearest neighbors in a dissimilarity matrix

Description

Usage

Value

Arguments

Author

Details

See Also

Examples