Projeto de people analytics, utilizando machine learning na clusterização de dados de funcionários que poderam solicitar demissão de seu trabalho.

A repository for Husband & McIntyre Texas U.S.A. Panhandle Odonata code.

Cluster sets of histograms/curves, in particular kinematic distributions in high energy physics.

Analysis of bond characteristic in high drug-likeness score compound

PICAFlow: a complete R workflow dedicated to flow/mass cytometry data, from data pre-processing to deep and comprehensive analysis.

El archivo NCI.txt contiene 6830 variables de 64 células cancerígenas de distintos tipos de cáncer. Mediante un análisis de conglomerados estudiaremos el posible agrupamiento de las células cancerígenas.

IU Projects

Credit scoring and segmentation refer to the process of evaluating the creditworthiness of individuals or businesses and dividing them into distinct groups based on their credit profiles.

A high performance implementation of HDBSCAN clustering.

A general purpose Snakemake workflow to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.

This project aims to segment customers of a mall into distinct groups based on their demographic and spending behavior. By leveraging K-Means clustering, the goal is to enable targeted marketing strategies and personalised customer experiences.

Unsupervised learning! Using Principal component analysis for dimension reduction and Clustering analysis.

Cluster analysis for mlr3

R markdown files employed in my research

R package for OpenCPU backend to detect and visualize disease clusters from the web

KubeEye aims to find various problems on Kubernetes, such as application misconfiguration, unhealthy cluster components and node problems.

An R package for clustering longitudinal datasets in a standardized way, providing interfaces to various R packages for longitudinal clustering, and facilitating the rapid implementation and evaluation of new methods

This project aims to predict online shoppers' purchase intentions using browsing history and user data from e-commerce sites. By analyzing clickstream and session information, the goal is to create a machine learning model that accurately forecasts customers' likelihood of making a purchase.

DenMune is a clustering algorithm that can find clusters of arbitrary size, shapes and densities in two-dimensions. Higher dimensions are first reduced to 2-D using the t-sne. The algorithm relies on a single parameter K (the number of nearest neighbors). The results show the superiority of DenMune. Enjoy the simplicty but the power of DenMune.

Balanced k-Means Revisited algorithm