Quickly search, compare, and analyze genomic and metagenomic data sets.
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Updated
Jun 9, 2024 - Python
Quickly search, compare, and analyze genomic and metagenomic data sets.
Elasticsearch plugin for b-bit minhash algorism
Dynatrace hash library for Java
MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW
JS implementation of probabilistic data structures: Bloom Filter (and its derived), HyperLogLog, Count-Min Sketch, Top-K and MinHash
Python package for fast MinHash calculation and operations
C++ Implementations of sketch data structures with SIMD Parallelism, including Python bindings
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
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.
Minhash and maxhash library in Python, combining flexibility, expressivity, and performance.
Development of an interactive system for restaurant recommendation, utilizing filtering algorithms like MinHash and Bloom Filter for analysis and personalized suggestions based on user evaluations.
Chiral version of the MinHashed Atom-Pair Fingerprint
A database for signatures of public genomic sources
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.
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
Approximate document similarity with Minhash + Locality Sensitive Hashing
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