A multimedia search engine built using face embeddings and multidimensional indexing techniques for efficient retrieval of face images

This repository is a related to all about Natural Langauge Processing - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)

Website for "Awesome Learning to Hash" https://learning2hash.github.io

Elasticsearch plugin for nearest neighbor search. Store vectors and run similarity search using exact and approximate algorithms.

Open source audio fingerprinting in .NET. An efficient algorithm for acoustic fingerprinting written purely in C#.

An implementation of Anchor Graph Hashing (Liu et al. 2011) in Python.

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

Fast and precise comparison of genomes and metagenomes (in the order of terabytes) on a typical personal laptop

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

ANN - Approximate Nearest Neighbors Index with Locality Sensitive Hashing and Hyper Cube projections for vectors and multi-dimensional data.

A learning algorithm for locality-sensitive bucketing functions

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.

Multi module project focused on near-duplicate search for images.

locality sensitive hashing (LSHASH) for Python3

Python library for detecting near duplicate texts in a corpus at scale using Locality Sensitive Hashing, as described in chapter three of Mining Massive Datasets.

Explores the MovieLens dataset (1M version) to uncover valuable insights into user behavior, demographics, movie popularity, and community structures. Various tasks, including data preprocessing, clustering, community detection, and recommendation systems, provide a holistic understanding of the dataset.

Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

Minhash and maxhash library in Python, combining flexibility, expressivity, and performance.

This repository contains code and analysis for a homework assignment on recommendation systems and clustering algorithms in Python. Implements techniques like minhash, LSH, feature engineering, dimensionality reduction, K-means and DBSCAN clustering.