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Clustering algorithm implementaions from scratch with python (k-means, EM-GMM, mean-shift, agglomerative)

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Name		Name	Last commit message	Last commit date
Latest commit History 29 Commits
data_2d_test		data_2d_test
test_2d_visualization_results		test_2d_visualization_results
Agglomerative.py		Agglomerative.py
GaussianMM.py		GaussianMM.py
GaussianMM_Ver0.py		GaussianMM_Ver0.py
KMeans.py		KMeans.py
KMeans_Ver0.py		KMeans_Ver0.py
MeanShift.py		MeanShift.py
README.md		README.md
test_2d_visualization.py		test_2d_visualization.py

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Clustering algorithm implementaion and visualization from scratch with python

Four popular clustering algorithms (for d >= 2 dimensions, k >= 2 clusters):

(1) k-means clustering
(2) Gaussian mixture model - expectation maximization algorithm (EM-GMM)
(3) mean-shift clustering
(4) agglomerative clustering

Python implementations:

KMeans.py: k-means clustering
KMeans_Ver0.py: second version of k-means implementation as a function
GaussianMM.py: EM-GMM
GaussianMM_Ver0.py: second version of EM-GMM implementation with functions of AIC, BIC and predict
MeanShift.py: mean-shift clustering
Agglomerative: agglomerative clustering

Evaluations and tests:

test_2d_visualization.py: tests on 2D datasets with visualization, compared with sklearn implementation
data_2d_test folder: datasets for tests
test_2d_visualization_results folder: output images of tests

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Clustering algorithm implementaions from scratch with python (k-means, EM-GMM, mean-shift, agglomerative)

python machine-learning data-mining clustering image-segmentation kmeans-clustering agglomerative-clustering gmm-clustering meanshift-clustering

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