Neighbor Search and Clustering for Vectors using Locality-sensitive hashing and Randomized Projection to Hypercube
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Updated
Mar 24, 2022 - C
Neighbor Search and Clustering for Vectors using Locality-sensitive hashing and Randomized Projection to Hypercube
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 )
📈|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.
Neighbor Search and Clustering for Time-Series using Locality-sensitive hashing and Randomized Projection to Hypercube. Time series comparison is performed using Discrete Frechet or Continuous Frechet metric.
Home built 3D printers related files (firmware, hardware, custom parts...)
Vectors - Nearest neighbor search and Clustering using LSH, Hypercube (and Lloyd's only at the clustering) algorithms with L2 metric.
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