m_psych_embeds

<p>Data containing Glove embeddings of psychological related
words, useful for demonstrating the use of ensemble metrics.</p>

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

m_psych_embeds function

<p>A matrix with 831 words in rows and 100 embedding dimensions in
columns.</p>

m_psych_embeds: A dataset containing the embeddings matrix of psychological related words

Description

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