random-partitions

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cissagatto / HPML

This repository hold all experiments conducted during my PhD (2019-2023). HPML means "Hybrid Partitions for Multi-Label Classification". SET-UP-1

ecc partitioning partitions multi-label-classification multi-label clus partitioning-label-space label-correlations label-space random-partitions multi-label-partitions community-detection-methods hybrid-partitions correlations-between-labels modeling-label-correlations exploring-label-correlations self-organized-maps aglomerative-hierarchical-clustering

Updated Sep 24, 2024

cissagatto / Generate-Partitions-Random3

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This code is part of my doctoral research. The aim is to generate a specific version of random partitions for multilabel classification.

random supervised-learning classification partitions multi-label-classification multi-label multilabel clus random-partitions multi-label-partitions

Updated Oct 30, 2023
R

Implemented KMeans from scratch and trained it on Fashion-MNIST dataset by experimenting with initializaion methods like forgy, random partitions, kmeans++ and found the optimal number of clusters by implementing elbow & silhouette algorithms from scratch

kmeans-clustering elbow-method fashion-mnist kmeans-plus-plus scratch-implementation silhouette-score random-partitions forgy-initialization

Updated Aug 17, 2021
Jupyter Notebook

cissagatto / Generate-Partitions-Random1

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This code is part of my doctoral research. The aim is to generate a specific version of random partitions for multilabel classification.

machine-learning r classification partitions multi-label-classification multi-label random-partitions multi-label-partitions multi-label-random-partitions

Updated Oct 30, 2023
R

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