Projects that are tagged with clustering.
Showing Items 1-20 of 46 on page 1 of 3: 1 2 3 Next

Logo MLweb 0.1.4

by lauerfab - June 28, 2016, 16:00:52 CET [ Project Homepage BibTeX Download ] 4001 views, 977 downloads, 3 subscriptions

About: MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes (i) a javascript library to enable scientific computing within web pages, (ii) a javascript library implementing machine learning algorithms for classification, regression, clustering and dimensionality reduction, (iii) a web application providing a matlab-like development environment.

Changes:
  • Add Logistic Regression
  • Add support for sparse input in fast training of linear SVM
  • Better support for sparse vectors/matrices
  • Fix plot windows in IE
  • Minor bug fixes

Logo JMLR dlib ml 19.0

by davis685 - June 25, 2016, 23:04:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 142344 views, 23204 downloads, 4 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

Changes:

This release adds a deep learning toolkit to dlib that has a clean and fully documented C++11 API. It also includes CPU and GPU support, binds to cuDNN, can train on multiple GPUs at a time, and comes with a pretrained imagenet model based on ResNet34.

The release also adds a number of other improvements such as new elastic net regularized solvers and QP solvers, improved MATLAB binding tools, and other usability tweaks and optimizations.


Logo ADENINE 0.1.3

by samuelefiorini - June 13, 2016, 11:10:36 CET [ Project Homepage BibTeX Download ] 418 views, 67 downloads, 2 subscriptions

About: ADENINE (A Data ExploratioN pIpeliNE) is a machine learning framework for data exploration that encompasses state-of-the-art techniques for missing values imputing, data preprocessing, dimensionality reduction and clustering tasks.

Changes:

Initial Announcement on mloss.org.


Logo WEKA 3.9.0

by mhall - April 15, 2016, 06:35:30 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 58749 views, 8757 downloads, 5 subscriptions

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About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

Changes:

In core weka:

  • JAMA-based linear algebra routines replaced with MTJ. Faster operation with the option to use native libraries for even more speed
  • General efficiency improvements in core, filters and some classifiers
  • GaussianProcesses now handles instance weights
  • New Knowledge Flow implementation. Engine completely rewritten from scratch with a simplified API
  • New Workbench GUI
  • GUI package manager now has a search facility
  • FixedDictionaryStringToWordVector filter allows the use of an external dictionary for vectorization. DictionarySaver converter can be used to create a dictionary file

In packages:

  • Packages that were using JAMA are now using MTJ
  • New netlibNativeOSX, netlibNativeWindows and netlibNativeLinux packages providing native reference implementations (and system-optimized implementation in the case of OSX) of BLAS, LAPACK and ARPACK linear algebra
  • New elasticNet package, courtesy of Nikhil Kinshore
  • New niftiLoader package for loading a directory with MIR data in NIfTI format into Weka
  • New percentageErrorMetrics package - provides plugin evaluation metrics for root mean square percentage error and mean absolute percentage error
  • New iterativeAbsoluteErrorRegression package - provides a meta learner that fits a regression model to minimize absolute error
  • New largeScaleKernelLearning package - contains filters for large-scale kernel-based learning
  • discriminantAnalysis package now contains an implementation for LDA and QDA
  • New Knowledge Flow component implementations in various packages
  • newKnowledgeFlowStepExamples package - contains code examples for new Knowledge Flow API discussion in the Weka Manual
  • RPlugin updated to latest version of MLR
  • scatterPlot3D and associationRulesVisualizer packages updated with latest Java 3D libraries
  • Support for pluggable activation functions in the multiLayerPerceptrons package

Logo ELKI 0.7.1

by erich - March 14, 2016, 13:44:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20294 views, 3679 downloads, 4 subscriptions

About: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.

Changes:

Additions and improvements from ELKI 0.7.0 to 0.7.1:

Algorithm additions:

  • GriDBSCAN: DBSCAN using grid partitioning (Minkowski distances only)

  • Compare-Means and Sort-Means k-means variations (much faster than traditional k-means)

  • Visualization of dendrograms.

Important bug fixes:

  • Classes with no package ("default package") would cause errors.

  • The fast power function implementation was sometimes returning incorrect results.

  • Random sampling was sometimes not sampling from the full data set.

UI improvements:

  • The file input source will now automatically choose the Arff parser for .arff files.

  • MiniGUI now allows choosing other applications.

  • MiniGUI now displays the command line in a separate field.

  • MiniGUI displays an error message, if an incorrect classpath or JAyatana (on Ubuntu) is detected.

  • Export to png now works, we added a work-around for an open Batik bug.

Smaller changes:

  • Many smaller bug fixes.

  • C-Index for cluster evaluation now can process larger data sets.

  • OPTICS output of undefined reachability fixed.

  • External distance matrixes are easier to use and perform additional checks.

  • Precomputed distance matrixes can answer range and kNN queries.

  • Voronoi visualization can be switched in the menu now.

  • Improved backwards command line compatibility with additional aliases.

  • Added generated @since annotations in JavaDoc.

  • Many new unit tests, renamed to the Java conventions.

  • Low-level reading of service files, to have faster startup.


Logo libcluster 2.3

by dsteinberg - February 27, 2016, 00:36:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3932 views, 845 downloads, 3 subscriptions

About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.

Changes:

New maximum cluster argument for all algorithms. Also no more matlab interface since it seemed no one was using it, and I cannot support it any longer.


Logo APCluster 1.4.3

by UBod - February 25, 2016, 16:22:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 32281 views, 5646 downloads, 3 subscriptions

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About: The apcluster package implements Frey's and Dueck's Affinity Propagation clustering in R. The package further provides leveraged affinity propagation, exemplar-based agglomerative clustering, and various tools for visual analysis of clustering results.

Changes:
  • added optional color legend to heatmap plotting; in line with this change, some minor changes to the interface of the heatmap() function
  • corresponding updates of help pages and vignette

Logo KeLP 2.0.2

by kelpadmin - February 17, 2016, 09:03:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8096 views, 2062 downloads, 3 subscriptions

About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate prediction models without writing a single line of code.

Changes:

In addition to minor bug fixes, this release includes:

  • the Nystrom method for linearizing instances and allowing a large scale kernel learning

  • New examples for the usage of the Smoothed Partial Tree Kernel and the Compositionally Smoothed Partial Tree Kernel.

Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.0.2!


Logo Apache Mahout 0.11.1

by gsingers - November 9, 2015, 16:12:06 CET [ Project Homepage BibTeX Download ] 21335 views, 5559 downloads, 3 subscriptions

About: Apache Mahout is an Apache Software Foundation project with the goal of creating both a community of users and a scalable, Java-based framework consisting of many machine learning algorithm [...]

Changes:

Apache Mahout introduces a new math environment we call Samsara, for its theme of universal renewal. It reflects a fundamental rethinking of how scalable machine learning algorithms are built and customized. Mahout-Samsara is here to help people create their own math while providing some off-the-shelf algorithm implementations. At its core are general linear algebra and statistical operations along with the data structures to support them. You can use is as a library or customize it in Scala with Mahout-specific extensions that look something like R. Mahout-Samsara comes with an interactive shell that runs distributed operations on a Spark cluster. This make prototyping or task submission much easier and allows users to customize algorithms with a whole new degree of freedom. Mahout Algorithms include many new implementations built for speed on Mahout-Samsara. They run on Spark 1.3+ and some on H2O, which means as much as a 10x speed increase. You’ll find robust matrix decomposition algorithms as well as a Naive Bayes classifier and collaborative filtering. The new spark-itemsimilarity enables the next generation of cooccurrence recommenders that can use entire user click streams and context in making recommendations.


Logo Cognitive Foundry 3.4.2

by Baz - October 30, 2015, 06:53:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 27468 views, 4686 downloads, 4 subscriptions

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

Changes:
  • General:
    • Upgraded MTJ to 1.0.3.
  • Common:
    • Added package for hash function computation including Eva, FNV-1a, MD5, Murmur2, Prime, SHA1, SHA2
    • Added callback-based forEach implementations to Vector and InfiniteVector, which can be faster for iterating through some vector types.
    • Optimized DenseVector by removing a layer of indirection.
    • Added method to compute set of percentiles in UnivariateStatisticsUtil and fixed issue with percentile interpolation.
    • Added utility class for enumerating combinations.
    • Adjusted ScalarMap implementation hierarchy.
    • Added method for copying a map to VectorFactory and moved createVectorCapacity up from SparseVectorFactory.
    • Added method for creating square identity matrix to MatrixFactory.
    • Added Random implementation that uses a cached set of values.
  • Learning:
    • Implemented feature hashing.
    • Added factory for random forests.
    • Implemented uniform distribution over integer values.
    • Added Chi-squared similarity.
    • Added KL divergence.
    • Added general conditional probability distribution.
    • Added interfaces for Regression, UnivariateRegression, and MultivariateRegression.
    • Fixed null pointer exception that can happen in K-means with an empty cluster.
    • Fixed name of maxClusters property on AgglomerativeClusterer (was called maxMinDistance).
  • Text:
    • Improvements to LDA Gibbs sampler.

Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 1292 views, 257 downloads, 1 subscription

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.

Changes:

Initial Announcement on mloss.org.


Logo LMW Tree 1.0

by cdevries - May 30, 2015, 11:42:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1976 views, 384 downloads, 2 subscriptions

About: Learning M-Way Tree - Web Scale Clustering - EM-tree, K-tree, k-means, TSVQ, repeated k-means, clustering, random projections, random indexing, hashing, bit signatures

Changes:

Initial Announcement on mloss.org.


Logo ClusterEval 1.1

by cdevries - May 18, 2015, 22:01:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5063 views, 1220 downloads, 2 subscriptions

About: Cluster quality Evaluation software. Implements cluster quality metrics based on ground truths such as Purity, Entropy, Negentropy, F1 and NMI. It includes a novel approach to correct for pathological or ineffective clusterings called 'Divergence from a Random Baseline'.

Changes:

Moved project to GitHub.


Logo Auto encoder Based Data Clustering Toolkit 1.0

by openpr_nlpr - February 10, 2015, 08:30:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2263 views, 418 downloads, 2 subscriptions

About: The auto-encoder based data clustering toolkit provides a quick start of clustering based on deep auto-encoder nets. This toolkit can cluster data in feature space with a deep nonlinear nets.

Changes:

Initial Announcement on mloss.org.


Logo Hub Miner 1.1

by nenadtomasev - January 22, 2015, 16:33:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3701 views, 690 downloads, 2 subscriptions

About: Hubness-aware Machine Learning for High-dimensional Data

Changes:
  • BibTex support for all algorithm implementations, making all of them easy to reference (via algref package).

  • Two more hubness-aware approaches (meta-metric-learning and feature construction)

  • An implementation of Hit-Miss networks for analysis.

  • Several minor bug fixes.

  • The following instance selection methods were added: HMScore, Carving, Iterative Case Filtering, ENRBF.

  • The following clustering quality indexes were added: Folkes-Mallows, Calinski-Harabasz, PBM, G+, Tau, Point-Biserial, Hubert's statistic, McClain-Rao, C-root-k.

  • Some more experimental scripts have been included.

  • Extensions in the estimation of hubness risk.

  • Alias and weighted reservoir methods for weight-proportional random selection.


Logo Accord.NET Framework 2.14.0

by cesarsouza - December 9, 2014, 23:04:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 29601 views, 5409 downloads, 2 subscriptions

About: The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details.

Changes:

Adding a large number of new distributions, such as Anderson-Daring, Shapiro-Wilk, Inverse Chi-Square, Lévy, Folded Normal, Shifted Log-Logistic, Kumaraswamy, Trapezoidal, U-quadratic and BetaPrime distributions, Birnbaum-Saunders, Generalized Normal, Gumbel, Power Lognormal, Power Normal, Triangular, Tukey Lambda, Logistic, Hyperbolic Secant, Degenerate and General Continuous distributions.

Other additions include new statistical hypothesis tests such as Anderson-Daring and Shapiro-Wilk; as well as support for all of LIBLINEAR's support vector machine algorithms; and format reading support for MATLAB/Octave matrices, LibSVM models, sparse LibSVM data files, and many others.

For a complete list of changes, please see the full release notes at the release details page at:

https://github.com/accord-net/framework/releases


Logo libAGF 0.9.8

by Petey - December 6, 2014, 02:35:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14776 views, 2842 downloads, 2 subscriptions

About: C++ software for statistical classification, probability estimation and interpolation/non-linear regression using variable bandwidth kernel estimation.

Changes:

New in Version 0.9.8:

  • bug fixes: svm file conversion works properly and is more general

  • non-hierarchical multi-borders has 3 options for solving for the conditional probabilities: matrix inversion, voting, and matrix inversion over-ridden by voting, with re-normalization

  • multi-borders now works with external binary classifiers

  • random numbers resolve a tie when selecting classes based on probabilities

  • pair of routines, sort_discrete_vectors and search_discrete_vectors, for classification based on n-d binning (still experimental)

  • command options have been changed with many new additions, see QUICKSTART file or run the relevant commands for details


Logo The Statistical ToolKit 0.8.4

by joblion - December 5, 2014, 13:21:47 CET [ Project Homepage BibTeX Download ] 2570 views, 739 downloads, 2 subscriptions

About: STK++: A Statistical Toolkit Framework in C++

Changes:

Inegrating openmp to the current release. Many enhancement in the clustering project. bug fix


Logo pSpectralClustering 1.1

by tbuehler - July 30, 2014, 19:44:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8163 views, 1813 downloads, 2 subscriptions

About: A generalized version of spectral clustering using the graph p-Laplacian.

Changes:
  • fixed compatibility issue with Matlab R2013a+
  • several internal optimizations

Logo DRVQ 1.0.1-beta

by iavr - January 18, 2014, 17:26:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2689 views, 609 downloads, 1 subscription

About: DRVQ is a C++ library implementation of dimensionality-recursive vector quantization, a fast vector quantization method in high-dimensional Euclidean spaces under arbitrary data distributions. It is an approximation of k-means that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a by-product of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast.

Changes:

Initial Announcement on mloss.org.


Showing Items 1-20 of 46 on page 1 of 3: 1 2 3 Next