Search and clustering vectors in C++

approximation algorithms for exact nearest neighbors search and clustering on multi-dimensional vectors

Software Development for Algorithmic Problems (UoA) Assignments

Algorithmic problem-solving project using autoencoders for dimension reduction, nearest neighbor search algorithms, and K-means clustering. Developed as part of a university course on software development for algorithmic problems.

Clustering methods implementations in C++: Lloyd, K-Means, K-Means++, PAM

This project was developed in the scope of the undergraduate course algorithmic problem solving of the NKUA. It includes 12 different implementations of clustering techniques from simple K-means to LSH/Hypercube clustering.

A rotating translucent four dimensions hypercube

My flavor of Marlin for my Hypercube Evolution build, running off of an MKS Robin Nano v1.2 with a 300mm^3 build volume.

Testing the effectiveness of Approximate k-NN search with LSH and Hypercube on MNIST. Also, implemented k-medians++ for the same dataset.

Nearest neighbor search. Methods: LSH, hypercube, and exhaustive search. C++

First assignment for the University Senior Project course

Comparison of multiple methods for calculating MNIST hand-written digits similarity.

📈 kNN using LSH and Hypercube projection & Clustering using kMeans++ for n-dim polygonal curves and time series

Visualiser for basic geometric primitives and fractals in arbitrary-dimensional spaces

LSH and Hypercube algorithms for Approximate Nearest Neighbor. Centroid based clustering using Lloyd's and reverse assignment algorithms.