Showing 11 open source projects for "clustering algorithm"

View related business solutions
  • Lightspeed golf course management software Icon
    Lightspeed golf course management software

    Lightspeed Golf is all-in-one golf course management software to help courses simplify operations, drive revenue and deliver amazing golf experiences.

    From tee sheet management, point of sale and payment processing to marketing, automation, reporting and more—Lightspeed is built for the pro shop, restaurant, back office, beverage cart and beyond.
    Learn More
  • Business Automation Software for SMBs Icon
    Business Automation Software for SMBs

    Fed up with not having the time, money and resources to grow your business?

    The only software you need to increase cash flow, optimize resource utilization, and take control of your assets and inventory.
    Learn More
  • 1
    HDBSCAN

    HDBSCAN

    A high performance implementation of HDBSCAN clustering

    HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster size, is intuitive and easy to select. ...
    Downloads: 1 This Week
    Last Update:
    See Project
  • 2
    sktime

    sktime

    A unified framework for machine learning with time series

    ...It features dedicated time series algorithms and tools for composite model building such as pipelining, ensembling, tuning, and reduction, empowering users to apply an algorithm designed for one task to another.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    Homemade Machine Learning

    Homemade Machine Learning

    Python examples of popular machine learning algorithms

    homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 4
    ML for Beginners

    ML for Beginners

    12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

    ML-For-Beginners is a structured, project-driven curriculum that teaches foundational machine learning concepts with approachable math and lots of code. Organized as a multi-week course, it mixes short lectures with labs in notebooks so learners practice regression, classification, clustering, and recommendation techniques on real datasets. Each lesson aims to connect the algorithm to a relatable scenario, reinforcing intuition before diving into parameters, metrics, and trade-offs. The repository includes quizzes, solutions, and instructor materials to make the content usable in classrooms or self-study. It emphasizes ethical considerations and model evaluation—accuracy is not the only metric—so students learn to validate and communicate results responsibly. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • Loan management software that makes it easy. Icon
    Loan management software that makes it easy.

    Ideal for lending professionals who are looking for a feature rich loan management system

    Bryt Software is ideal for lending professionals who are looking for a feature rich loan management system that is intuitive and easy to use. We are 100% cloud-based, software as a service. We believe in providing our customers with fair and honest pricing. Our monthly fees are based on your number of users and we have a minimal implementation charge.
    Learn More
  • 5
    DeepCluster

    DeepCluster

    Deep Clustering for Unsupervised Learning of Visual Features

    DeepCluster is a classic self-supervised clustering-based representation learning algorithm that iteratively groups image features and uses the cluster assignments as pseudo-labels to train the network. In each round, features produced by the network are clustered (e.g. k-means), and the cluster IDs become supervision targets in the next epoch, encouraging the model to refine its representation to better separate semantic groups.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 6

    QSdpR

    Viral Quasispecies Reconstruction software based on QSdpR algorithm

    This is a viral quasispecies reconstruction software for quasispecies assembly problem on mRNA viruses, which is based on a correlation clustering approach and uses semidefinite optimization framework. The software accepts a reference genome, a NGS read set aligned to this reference and set of SNP locations in the form of a vcf file and outputs an optimal set of reconstructed species genomes which describes the underlying viral population.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 7
    node2vec

    node2vec

    Learn continuous vector embeddings for nodes in a graph using biased R

    The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. ...
    Downloads: 0 This Week
    Last Update:
    See Project
  • 8

    Unsupervised Random Forest

    On-line Unsupervised Random Forest

    ...It supports on-line prediction of new observations (no need to retrain); and supports datasets that contain both continuous (e.g. CPU load) and categorical (e.g. VM instance type) features. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency and meeting the dynamism requirement of autonomic clouds, our methodology consists of two steps: (i) off-line clustering and (ii) on-line prediction. RF+PAM can: Cluster observations (Unsupervised Learning) Calculate the dissimilarity between 2 or more observations (how different two observations are)
    Downloads: 0 This Week
    Last Update:
    See Project
  • 9
    Stanford Machine Learning Course

    Stanford Machine Learning Course

    machine learning course programming exercise

    ...It includes implementations of a variety of fundamental algorithms using Python and MATLAB/Octave. The repository covers a broad set of topics such as linear regression, logistic regression, neural networks, clustering, support vector machines, and recommender systems. Each folder corresponds to a specific algorithm or concept, making it easy for learners to navigate and practice. The exercises serve as practical, hands-on reinforcement of theoretical concepts taught in the course. This collection is valuable for students and practitioners who want to strengthen their skills in machine learning through coding exercises.
    Downloads: 12 This Week
    Last Update:
    See Project
  • Inventors: Validate Your Idea, Protect It and Gain Market Advantages Icon
    Inventors: Validate Your Idea, Protect It and Gain Market Advantages

    SenseIP is ideal for individual inventors, startups, and businesses

    senseIP is an AI innovation platform for inventors, automating any aspect of IP from the moment you have an idea. You can have it researched for uniqueness and protected; quickly and effortlessly, without expensive attorneys. Built for business success while securing your competitive edge.
    Learn More
  • 10
    PocketAnalyzerPCA combines a geometric algorithm for detecting pockets in proteins with Principal Component Analysis and clustering. This enables visualization and analysis of pocket conformational distributions of large sets of protein structures.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 11
    Perform local clustering on vector data. Alternatively: psi_square, psisquare
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next