Unsupervised-Learning

Clustering Algorithms based on centroids namely K-Means Clustering, Agglomerative Clustering and Density Based Spatial Clustering implementation.

Requirements

K-Means Clustering done with smart convergence for faster processing and run multiple times to get the best result. Quite a few parameters to play around with and also with visualizing part.
Agglomerative Clustering is type of Hierarchical clustering (bottom up approach, grouping) which is popular than Divisive Clustering. Using distance matrix can derive dendrogram of the data, Criterion for calculating distance matrix can be changed and tested (single, average, complete and centroid linkage).
Density Based Spatial Clustering with Noise defines cluster as maximal set of density connected points. Functional program to test different parameters and datasets with the program and how long it takes to perform the operation.

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
.gitignore		.gitignore
Density-Based Clustering.py		Density-Based Clustering.py
Hierarchical Clustering.py		Hierarchical Clustering.py
K-Means Clustering.py		K-Means Clustering.py
LICENSE		LICENSE
README.md		README.md