Deep_Dive_into_the_Clustering_World

PROJECT SCOPE: Deepening in the main Clustering Techniques.

PROJECT RESULTS:

Data collection through the Performance Monitoring Windows Application (i.e., Built-in dataset).
Number of components choice through Elbow Method and Silhouette Coefficient.
K-Means Clustering Deepening: Lloyd and Elkan Algorithm. K-Means++ and Naïve Sharding Initialization.
K-Medians Clustering Deepening.
K-Medoids Clustering Deepening: PAM, Voronoi Iteration, CLARA and CLARANS Algorithms.
Mean-Shift Deepening: Object Tracking and Image Segmentation Applications.
DBSCAN Deepening.
GMM Deepening.
Deepening evaluation Silhouette Score, Accuracy, Purity, Rand Index, Adjusted Rand Index, Davies-Bouldin Index.
Final Decision Analysis of the best algorithm given my collected data.

PROJECT REPOSITORY: https://github.com/alessioborgi/Deep_Dive_into_the_Clustering_World

Name		Name	Last commit message	Last commit date
Latest commit History 20 Commits
Dataset		Dataset
Object_Tracking_Mean_Shift_Application		Object_Tracking_Mean_Shift_Application
figs		figs
Deep_Dive_Into_The_Clustering_World.css		Deep_Dive_Into_The_Clustering_World.css
Deep_Dive_Into_The_Clustering_World.ipynb		Deep_Dive_Into_The_Clustering_World.ipynb
README.md		README.md
gmm.csv		gmm.csv
kmeans.csv		kmeans.csv
kmeansplus.csv		kmeansplus.csv
logo_sapienza.png		logo_sapienza.png

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