A novel Clustering algorithm by measuring Direction Centrality (CDC) locally. It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points.

The project classifies characters (Hand-Written) by three algorithms Weighted kNN, Bayesian and SVM (using MATLAB R2016b and App Design GUI)

Test my MNIST data using kNN

Implementation of the knn algorithm to generate decision regions using the Iris dataset.

KNN Algorithms, Naive Bayes, Classification Tree, PCA Implementations

Patient subgrouping with distinct survival rates via integration of multiomics data on a Grassmann manifold