Showing 5 open source projects for "clustering algorithm matlab"

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  • 1
    hctsa

    hctsa

    Highly comparative time-series analysis

    hctsa is a Matlab software package for running highly comparative time-series analysis. It extracts thousands of time-series features from a collection of univariate time series and includes a range of tools for visualizing and analyzing the resulting time-series feature matrix.
    Downloads: 1 This Week
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  • 2
    TIGRE

    TIGRE

    TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox

    ...Its focus is on iterative algorithms for improved image quality that have all been optimized to run on GPUs (including multi-GPUs) for improved speed. It combines the higher-level abstraction of MATLAB or Python with the performance of CUDA at a lower level in order to make it both fast and easy to use. TIGRE is free to download and distribute: use it, modify it, add to it, and share it. Our aim is to provide a wide range of easy-to-use algorithms for the tomographic community "off the shelf". We would like to build a stronger bridge between algorithm developers and imaging researchers/clinicians by encouraging and supporting contributions from both sides to TIGRE.
    Downloads: 1 This Week
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  • 3
    Visualization and clustering analysis of X-Ray diffraction data from combinatorial thin film libraries.
    Downloads: 0 This Week
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  • 4
    LAVA for AFM

    LAVA for AFM

    Lloyd's Algorithm-based Volumetric Analysis for AFM Images

    This MATLAB-based program enables analysis of AFM images. In particular, it allows the user to measure the volume of all distinguishable features in an image and to isolate and sort those particles. As the name implies, LAVA uses Lloyd's Algorithm, also known as k-means sorting, to identify a small set of volumes representative of the actual distribution of particle in the image.
    Downloads: 0 This Week
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  • 5
    The Databionics ESOM Tools offer many data mining tasks using Emergent Self-Organizing Maps. Visualization, clustering, and classification of high-dimensional data using databionics principles can be performed interactively or automatically.
    Downloads: 0 This Week
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