search.neighbour

search.neighbour give the id of the Kth nearest neighbour

internal

Graphical toolbox for clustering and classification of data frames.
It proposes a graphical interface to process clustering and classification methods on features
data-frames, and to view initial data as well as resulted cluster or classes. According to the
level of available labels, different approaches are proposed: unsupervised clustering,
semi-supervised clustering and supervised classification.
To assess the processed clusters or classes, the toolbox can import and show some supplementary
data formats: either profile/time series, or images.
These added information can help the expert to label clusters (clustering), or to constrain data
frame rows (semi-supervised clustering), using Constrained spectral embedding algorithm by
Wacquet et al. (2013) <doi:10.1016/j.patrec.2013.02.003> and the methodology provided by
Wacquet et al. (2013) <doi:10.1007/978-3-642-35638-4_21>.

Pierre-Alexandre Hebert

RclusTool

Graphical Toolbox for Clustering and Classification of Data
Frames

Guillaume Wacquet

Emilie Poisson

Pierre Talon

search.neighbour function

<dl><dt>vdist</dt>
<dd>distance vector of point with other points</dd>
<dt>k</dt>
<dd>number of neighbours</dd></dl>

Arguments

Search neighbour — search.neighbour

<dl>

<dt>vdist</dt>
<dd>distance vector of point with other points</dd>


<dt>k</dt>
<dd>number of neighbours</dd>

</dl>

search.neighbour: Search neighbour

Description

Usage

Value

Arguments

See Also

Examples