iGecco

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data

iGecco studies the problem of integrative clustering in data integration, where we observe data collected on common samples, but with features from multiple sources of data sets and want to cluster those common samples. This repository provides the codes associated with Minjie Wang and Genevera I. Allen "Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data" (2020+).

Directory Structure

• iGecco+: contains codes for Integrative Generalized Convex Clustering Optimization with Feature Selection (iGecco+)

How to Use

• Integrative Generalized Convex Clustering Optimization with Feature Selection (iGecco+):

1. Download repository of iGecco+
2. Run example usage file: example_code.m