About: This toolbox implements a novel visualization technique called Sectors on Sectors (SonS), and a extended version called Multidimensional Sectors on Sectors (MDSonS), for improving the interpretation of several data mining algorithms. The MDSonS method makes use of Multidimensional Scaling (MDS) to solve the main drawback of the previous method, namely, the lack of representing distances between pairs of clusters. These methods have been applied for visualizing the results of hierarchical clustering, Growing Hierarchical Self-Organizing Maps (GHSOM), classification trees and several manifolds. These methods make possible to extract all the existing relationships among centroids’ attributes at any hierarchy level.

Initial Announcement on mloss.org.

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'.

Initial Announcement on mloss.org.

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.

• plotting of clustering results extended to data sets with more than two dimensions (resulting in the clustering result being superimposed in a scatterplot matrix); the variant that plot() can be used to draw a heatmap has been removed. From now on, heatmap() must always be used.
• improved NA handling
• correction of input check in apcluster() and apclusterL() (previously, both functions issued a warning whenever argument p had length > 1)
• corresponding updates and further improvements of help pages and vignette

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

• General:
  • Made code able to compile under both Java 1.6 and 1.7. This required removing some potentially unsafe methods that used varargs with generics.
  • Upgraded XStream dependency to 1.4.4.
  • Improved support for regression algorithms in learning.
  • Added general-purpose adapters to make it easier to compose learning algorithms and adapt their input or output.
• Common Core:
  • Added isSparse, toArray, dotDivide, and dotDivideEquals methods for Vector and Matrix.
  • Added scaledPlus, scaledPlusEquals, scaledMinus, and scaledMinusEquals to Ring (and thus Vector and Matrix) for potentially faster such operations.
  • Fixed issue where matrix and dense vector equals was not checking for equal dimensionality.
  • Added transform, transformEquals, tranformNonZeros, and transformNonZerosEquals to Vector.
  • Made LogNumber into a signed version of a log number and moved the prior unsigned implementation into UnsignedLogNumber.
  • Added EuclideanRing interface that provides methods for times, timesEquals, divide, and divideEquals. Also added Field interface that provides methods for inverse and inverseEquals. These interfaces are now implemented by the appropriate number classes such as ComplexNumber, MutableInteger, MutableLong, MutableDouble, LogNumber, and UnsignedLogNumber.
  • Added interface for Indexer and DefaultIndexer implementation for creating a zero-based indexing of values.
  • Added interfaces for MatrixFactoryContainer and DivergenceFunctionContainer.
  • Added ReversibleEvaluator, which various identity functions implement as well as a new utility class ForwardReverseEvaluatorPair to create a reversible evaluator from a pair of other evaluators.
  • Added method to create an ArrayList from a pair of values in CollectionUtil.
  • ArgumentChecker now properly throws assertion errors for NaN values. Also added checks for long types.
  • Fixed handling of Infinity in subtraction for LogMath.
  • Fixed issue with angle method that would cause a NaN if cosine had a rounding error.
  • Added new createMatrix methods to MatrixFactory that initializes the Matrix with the given value.
  • Added copy, reverse, and isEmpty methods for several array types to ArrayUtil.
  • Added utility methods for creating a HashMap, LinkedHashMap, HashSet, or LinkedHashSet with an expected size to CollectionUtil.
  • Added getFirst and getLast methods for List types to CollectionUtil.
  • Removed some calls to System.out and Exception.printStackTrace.
• Common Data:
  • Added create method for IdentityDataConverter.
  • ReversibleDataConverter now is an extension of ReversibleEvaluator.
• Learning Core:
  • Added general learner transformation capability to make it easier to adapt and compose algorithms. InputOutputTransformedBatchLearner provides this capability for supervised learning algorithms by composing together a triplet. CompositeBatchLearnerPair does it for a pair of algorithms.
  • Added a constant and identity learners.
  • Added Chebyshev, Identity, and Minkowski distance metrics.
  • Added methods to DatasetUtil to get the output values for a dataset and to compute the sum of weights.
  • Made generics more permissive for supervised cost functions.
  • Added ClusterDistanceEvaluator for taking a clustering that encodes the distance from an input value to all clusters and returns the result as a vector.
  • Fixed potential round-off issue in decision tree splitter.
  • Added random subspace technique, implemented in RandomSubspace.
  • Separated functionality from LinearFunction into IdentityScalarFunction. LinearFunction by default is the same, but has parameters that can change the slope and offset of the function.
  • Default squashing function for GeneralizedLinearModel and DifferentiableGeneralizedLinearModel is now a linear function instead of an atan function.
  • Added a weighted estimator for the Poisson distribution.
  • Added Regressor interface for evaluators that are the output of (single-output) regression learning algorithms. Existing such evaluators have been updated to implement this interface.
  • Added support for regression ensembles including additive and averaging ensembles with and without weights. Added a learner for regression bagging in BaggingRegressionLearner.
  • Added a simple univariate regression class in UnivariateLinearRegression.
  • MultivariateDecorrelator now is a VectorInputEvaluator and VectorOutputEvaluator.
  • Added bias term to PrimalEstimatedSubGradient.
• Text Core:
  • Fixed issue with the start position for tokens from LetterNumberTokenizer being off by one except for the first one.

About: MLDemos is a user-friendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning.

New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance

New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)

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 [...]

http://sourceforge.net/projects/weka/files/weka-3-7/3.7.9/README-3-7-9.txt/view

About: Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...]

Added multi-target and multi-label learning, neural networks, Earth (MARS), PLS, and a faster tree induces for use in random forests; reorganization of module hierarchy; (weakly supported) Qwt has been replaced with a homemade module; networkx is used instead of a (weak) homemade structures for graphs; documentation has been moved to .rst, with a lot of it written anew or heavily redacted; improved system for registration of add-ons.

About: Automatic Analysis of Malware Behavior using Machine Learning

The tool's persistent state is stored in the local state directory (i.e. /var) for better maintenance. Several minor bugs have been fixed.

About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing.

CHANGES IN VERSION 2.4.0

o spfabia bugfixes

CHANGES IN VERSION 2.3.1

NEW FEATURES

o Getters and setters for class Factorization

2.0.0:

• spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
• fabia for non-negative factorization (parameter: non_negative);
• changed to C and removed dependencies to Rcpp;
• improved update for lambda (alpha should be smaller, e.g. 0.03);
• introduced maximal number of row elements (lL);
• introduced cycle bL when upper bounds nL or lL are effective;
• reduced computational complexity;
• bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;

1.4.0:

• New option nL: maximal number of biclusters per row element;
• Sort biclusters according to information content;
• Improved and extended preprocessing;
• Update to R2.13

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.

This is mostly a bug fix release. A lot of small issues have been fixed that improve performance, make error reporting a lot better, ease the use of sparse vectors and external precomputed distances, for example.

This will be the last ELKI release to support Java 6. The next ELKI release will require Java 7.

Algorithms

• Some new LOF variants (LDF, SimpleLOF, SimpleKernelDensityLOF)
• Correlation Outlier Probabilities (ICDM 2012)
• A naive mean-shift clustering
• Single-link clustering (SLINK algorithm) should be significantly faster due to optimized data structures
• "Benchmarking" algorithms for measuring the performance of index structures

Index layer

• Bulk loading R-Trees should be faster - in particular Sort Tile Recursive can work very well.
• M-Trees have been refactored and optimized for double distances

Database layer

• Bundle format (work in progress): low-level binary format for fast data exchange
• DBID and DataStore layer received some additional classes for further performance improvements
• KNN heap structures were revisited. The code is less clean now, but performs better in benchmarks.

Visualizations

• General clean up and API simplifications
• Some additional modules and improvements

Various

• There is a new parameter class, RandomParameter
• Some new distributions were added, also to the data set generator.

Tutorials

• The website has new tutorials, including one on a k-means variation that produces equal sized clusters.

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

New in Version 0.9.6:

• Many, many bug fixes. Thanks to all who submitted bug reports!

• New Bash script to validate probability density function (PDF) calculations: validate_pdf.sh

• New command for generating a simulated data set with approximately the same PDF as the training data: pdf_sim . Used by validate_pdf.sh

• New file utility that randomly splits up a dataset into two or more divisions: agf_split_data . Also used by validate_pdf.sh

• New command generates the Relative Operating Characteristic (ROC) curve: roc_curve

About: Divvy is a Mac OS X application for performing dimensionality reduction, clustering, and visualization.

Initial Announcement on mloss.org.

About: Fast C++ implementation of the variation of information (Meila 2003) and Rand index (Rand 1971) with MATLAB mex files

Initial Announcement on mloss.org.

About: mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL.

New features:

• LibSvm(): pred_probability() now returns probability estimates; pred_values() added
• LibLinear(): pred_values() and pred_probability() added
• dtw_std: squared Euclidean option added
• LCS for series composed by real values (lcs_real()) added
• Documentation

Fix:

• wavelet submodule: cwt(): it returned only real values in morlet and poul
• IRelief(): remove np. in learn()
• fix rfe_kfda and rfe_w2 when p=1

About: Implementation of the multi-assignment clustering method for Boolean vectors.

new bib added

About: An annotated java framework for machine learning, aimed at making it really easy to access analytically functions.

Now supports OLS and GLS regression and NaiveBayes classification

About: FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search.

See project page for changes.

About: The K-tree is a scalable approach to clustering inspired by the B+-tree and k-means algorithms.

Release of K-tree implementation in Python. This is targeted at a research and rapid prototyping audience.