Python code for common Machine Learning Algorithms

Python library for portfolio optimization built on top of scikit-learn

A Julia package for data clustering

A repository contains more than 12 common statistical machine learning algorithm implementations. 常见10余种机器学习算法原理与实现及视频讲解。@月来客栈 出品

Social Network Analysis and Visualization software application.

Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.

A Python implementation of divisive and hierarchical clustering algorithms. The algorithms were tested on the Human Gene DNA Sequence dataset and dendrograms were plotted.

Julia implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets.

Learning M-Way Tree - Web Scale Clustering - EM-tree, K-tree, k-means, TSVQ, repeated k-means, bitwise clustering

Implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTorch (NeurIPS 2021)

Algorithms and evaluation tools for extreme clustering

Genie: Fast and Robust Hierarchical Clustering

A fast approximation to a Dirichlet Process Mixture model (DPM) for clustering genetic data

Official PyTorch Implementation of HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization, CVPR 2023

Browser-based visualization tool that uses JSON and an interactive enclosure diagram to visualize networks.

Machine Learning Library, written in J

machine learning algorithms in Swift

A hierarchical agglomerative clustering (HAC) library written in C#

Hierarchical divisive clustering algorithm execution, visualization and Interactive visualization.

Interactively and visually explore large-scale image datasets used in machine learning using treemaps. VIS 2022

Self-Organizing Map [https://en.wikipedia.org/wiki/Self-organizing_map] is a popular method to perform cluster analysis. SOM shows two main limitations: fixed map size constraints how the data is being mapped and hierarchical relationships are not easily recognizable. Thus Growing Hierarchical SOM has been designed to overcome this issues

A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search

MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series - a PyTorch Version (AAAI-2023)

Collection of Artificial Intelligence Algorithms implemented on various problems

A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.

Hierarchical Clustering Algorithms

Obsidian plugin to export Graphviz graphs from vault's notes

An Interactive Approach to Understanding Unsupervised Learning Algorithms

Interactive tree-maps with SBERT & Hierarchical Clustering (HAC)

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