K Means Clustering Software

  • This is a variant of k-means algorithm which allows datas to belong to several clusters instead of just one..

    • Overlapping K-Means
    • Rousseau
    • Freeware (Free)
    • Windows
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  • This is the source code for Semi-supervised k-means clusrterer written in java, it implements the constrained k-means..

    • Freeware (Free)
    • Windows
  • ClusterMX is developed as a CLI-based tool that is able to implement various clustering algorithms. Among those algorithms we can find: · K-Means clustering optimized by random walks; · Weighted K-Means (applying force filed to the. ...

    • Freeware (Free)
    • Windows All
  • Generic C++ library for the training and evaluation of predictive and classification models from data matrices as used in bio- and chemoinformatics. SiMath provides a simple interface to a broad range of tools like SVM, PCA, SOM, k-means clustering.

    • Freeware (Free)
    • Windows
  • C# implementation of the K means clustering algorithm.

    • kmeans
    • DaveL
    • Freeware (Free)
    • Windows
  • Freeware for fast development and application of regression type networks including the multilayer perceptron, functional link net, piecewise linear network, self organizing map and K-Means. Extensive help. C source code for applying trained nets.

    • Shareware ($)
    • 13.84 Mb
    • 9x, NT, 2000, ME, XP
  • brCluster is a class library, written in java, that implements generic clustering algorithms carefully designed to allow its aplication in any kind of data. The algorithms implemented are K-means and Hierarchical Clustering (Simple and Complete. ...

    • Freeware (Free)
    • 3.63 Mb
    • Windows; Mac; Linux
  • clusterviz allows to cluster three-dimensional data. The clustering process is visualized using OpenGL. As clustering algorithms the family of k-means algorithms is implemented, including mixture. ...

    • Freeware (Free)
    • 23 Kb
    • Windows; Mac; Linux
  • K-tree provides a scalable approach to clustering inspired by the B+-tree and k-means algorithms. Clustering can be used to solve problems in signal processing, machine learning and other. ...

    • pyktree-0.4.1.tar.gz
    • ktree
    • Freeware (Free)
    • 22 Kb
    • Windows; Mac; Linux
  • Clustering problems are solved using various techniques such as SOM and K-Means. The generic problem involves multi-attribute sample points, with variable weights. We use Genetic Algorithms to build a scalable, generic & easy to use solution.

    • Discrete Data Categorization & Analysis
    • Amarnath Bose
    • Freeware (Free)
    • Windows
  • SLL is a C++ Statistical Learning Library. Many classical and modern learning algorithms are implemented, such as KNN, K-means, PCA, LDA, Spectral Clustering, Manifold Learning.

    • C++ Statistical Learning Library
    • sth4nth
    • Freeware (Free)
    • Windows
  • Cloudster is a generic and parallel implementation of the k-means algorithm. It is written in C# and uses the Windows Azure environment..

    • Cloudster-source.zip
    • cloudster
    • Freeware (Free)
    • 4.01 Mb
    • N/A

Related:  K Means Clustering - What Is Clustering - K Mean Clustering - Hierarchical Clustering Example - Hierarchical Clustering R

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