Update README.md

Author: DolbyUUU

README.md

@@ -1,20 +1,20 @@
 # Clustering algorithm implementaion and visualization from scratch with python
 
-Three popular clustering algorithms (for d >= 2 dimensions, k >= 2 clusters):  
+## 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 
 
-- (1) k-means  
-- (2) EM-GMM (Gaussian mixture model - expectation maximization algorithm)  
-- (3) mean-shift  
+## 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
 
-Python implementations:  
-
-- KMeans.py: k-means  
-- 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  
-
-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  
+## 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