TTAK.KO-12.0276 LSH Recursive Hasher

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 is a task using python to find number of similar songs within the provided songs set.

Implementation tasks for multiple algorithms to process massive data. The algorithms are written in Python.

Recommendation System on cryptocurrency, using data collected from users' tweets + 10-Fold Cross Validation ( Based on the cryptocoins from each user's tweets, the program runs algorithms on the data, resulting in the recommendation of other cryptocoins for each user) ( readme in greek but soon to be translated in English )

Image Retrieval implementation using Deep Learning and Kernelized Locality-Sensitive Hashing

📈|Time Series - Nearest neighbor search and Clustering using LSH, Hypercube (and Lloyd's only at the clustering) algorithms with metrics: L2, Discrete and Continuous Fréchet.

Vectors - Nearest neighbor search and Clustering using LSH, Hypercube (and Lloyd's only at the clustering) algorithms with L2 metric.

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

Image classification and unsupervised learning using latent space vectors produced by convolutional neural nets together with the original vectors space

A Robust Library in C# for Similarity Estimation

Dataset deduplication using the spark ML lib and Scala

distill large scale web page text

An implementation of Locality sensitive hashing

Search your object with hash

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