cosine_simi

Inputs will be L2 normalized, then matrix multiplied (y is transposed).
If second input is missing, first input will be recycled, which enables to
efficiently compute cosine similarities between the rows of a rectangular
matrix.

Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>.

Thomas Charlon

opticskxi

OPTICS K-Xi Density-Based Clustering

cosine_simi function

<dl><dt>x</dt>
<dd>Numeric vector or matrix</dd>
<dt>y</dt>
<dd>Numeric vector or matrix. If missing, copied from parameter x.</dd></dl>

Arguments

Cosine similarity between vectors and/or matrices. — cosine_simi

<dl>

<dt>x</dt>
<dd>Numeric vector or matrix</dd>


<dt>y</dt>
<dd>Numeric vector or matrix. If missing, copied from parameter x.</dd>

</dl>

cosine_simi: Cosine similarity between vectors and/or matrices.

Description

Usage

Value

Arguments