LSH: A Simple Shell

MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW

FAst Lookups of Cosine and Other Nearest Neighbors (based on fast locality-sensitive hashing)

Contrastive-LSH Embedding and Tokenization Technique for Multivariate Time Series Classification

Python package for fast MinHash calculation and operations

Multiple algorithms on KNN & Clustering on MNIST dataset implemented in C++ & .Jupyter Notebook

In this repository you can find an implementation of LSH (Local | Sensitive Hashing) and Finesse algorithms, designed to find similar data based on their hashes

This repository contains a web application that integrates with a music recommendation system, which leverages a dataset of 3,415 audio files, each lasting thirty seconds, utilising a Locality-Sensitive Hashing (LSH) implementation to determine rhythmic similarity, as part of an assignment for the Fundamental of Big Data Analytics (DS2004) course.

Light implementation of a shell written in C

Locality Sensitive Hashing, fuzzy-hash， min-hash, simhash, aHash, pHash, dHash。基于 Hash值的图片相似度、文本相似度

Python project for the Algorithmic Methods of Data Science class for the MSc. in Data Science at the Sapienza University of Rome. The main purpose of the project is building a Recommendation Engine using Locally Sensitive Hashing and using K-Means to cluster users based on their Netflix activity

Serverless, lightweight, and fast vector database on top of DynamoDB

A Robust Library in C# for Similarity Estimation

Weighted MinHash implementation on CUDA (multi-gpu).

Assessing MinHash LSH for text similarity. Compares with kNN using BART embeddings as ground truth. Involves data preprocessing, shingle creation, LSH experiments. Findings inform LSH's efficiency in document similarity tasks, enhancing understanding of LSH techniques.

cross-architecture binary comparison database

A backup suite. Supports FLZMA2, bzip3, LZ4, Zstandard, LSH i-node ordering deduplicating archiver, long range deduplication, encryption and recovery records

Fast reverse image search engine. Based on a custom fine-tuned version of ResNet-50 on the BAM! dataset. Similar image retrieval is performed using Locally Sensitive Hashing (LSH).

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

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