About: The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows.

• Added almost 20 more conversions and 20 new actors
• R-Project integration using Rserve
• WEKA webservice allows for programming language agnostic training, evaluation and use of WEKA models (classifiers, clusterers) and data processing using filters
• Spreadsheets now come with basic formula support
• Spreadsheets can be used for lookup tables in the flow
• Support for "chunked" reading/writing of spreadsheets to process millions of rows

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: An open source Java software providing collaborative filtering algorithms.

Initial Announcement on mloss.org.

About: MLwizard recommends and optimizes classification algorithms based on meta-learning and is a software wizard fully integrated into RapidMiner but can be used as library as well.

Faster parameter optimization using genetic algorithm with predefined start population.

About: Use the power of crowdsourcing to create ensembles.

Initial Announcement on mloss.org.

About: Motivated by a need to classify high-dimensional, heterogeneous data from the bioinformatics domain, we developed ML-Flex, a machine-learning toolbox that enables users to perform two-class and multi-class classification analyses in a systematic yet flexible manner. ML-Flex was written in Java but is capable of interfacing with third-party packages written in other programming languages. It can handle multiple input-data formats and supports a variety of customizations. MLFlex provides implementations of various validation strategies, which can be executed in parallel across multiple computing cores, processors, and nodes. Additionally, ML-Flex supports aggregating evidence across multiple algorithms and data sets via ensemble learning. (See http://jmlr.csail.mit.edu/papers/volume13/piccolo12a/piccolo12a.pdf.)

Initial Announcement on mloss.org.

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.

Changes in v.2.5.2 - faster version of quantile if multiple quantiles are requested - removes the dependency on ZLIB and thus - fixes "pkg install nan" for Octave on Windows - a number of minor improvements

For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG

About: The source code of the mldata.org site - a community portal for machine learning data sets.

Initial Announcement on mloss.org.

About: Tools to convert datasets from various formats to various formats, performance measures and API functions to communicate with mldata.org

• Change task file format, such that data splits can have a variable number items and put into up to 256 categories of training/validation/test/not used/...
• Various bugfixes.

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 [...]

We are pleased to announce release 0.4 of Mahout. Virtually every corner of the project has changed, and significantly, since 0.3. Developers are invited to use and depend on version 0.4 even as yet more change is to be expected before the next release. Highlights include:

* Model refactoring and CLI changes to improve integration and consistency
* New ClusterEvaluator and CDbwClusterEvaluator offer new ways to evaluate clustering effectiveness
* New Spectral Clustering and MinHash Clustering (still experimental)
* New VectorModelClassifier allows any set of clusters to be used for classification
* Map/Reduce job to compute the pairwise similarities of the rows of a matrix using a customizable similarity measure
* Map/Reduce job to compute the item-item-similarities for item-based collaborative filtering
* RecommenderJob has been evolved to a fully distributed item-based recommender
* Distributed Lanczos SVD implementation
* More support for distributed operations on very large matrices
* Easier access to Mahout operations via the command line
* New HMM based sequence classification from GSoC (currently as sequential version only and still experimental)
* Sequential logistic regression training framework
* New SGD classifier
* Experimental new type of NB classifier, and feature reduction options for existing one
* New vector encoding framework for high speed vectorization without a pre-built dictionary
* Additional elements of supervised model evaluation framework
* Promoted several pieces of old Colt framework to tested status (QR decomposition, in particular)
* Can now save random forests and use it to classify new data
* Many, many small fixes, improvements, refactorings and cleanup

About: Pyriel is a Python system for learning classification rules from data. Unlike other rule learning systems, it is designed to learn rule lists that maximize the area under the ROC curve (AUC) instead of accuracy. Pyriel is mostly an experimental research tool, but it's robust and fast enough to be used for lightweight industrial data mining.

1.5 Changed CF (confidence factor) to do LaPlace smoothing of estimates. New flag "--score-for-class C" causes scores to be computed relative to a given (positive) class. For two-class problems. Fixed bug in example sampling code (--sample n) Fixed bug keeping old-style example formats (terminated by dot) from working. More code restructuring.

About: This Java software implements Profile Hidden Markov Models (PHMMs) for protein classification for the WEKA workbench. Standard PHMMs and newly introduced binary PHMMs are used. In addition the software allows propositionalisation of PHMMs.

description changed

About: ELF provides many well implemented supervised learners for classification and regression tasks with an opportunity of ensemble learning.

Initial Announcement on mloss.org.

About: Debellor is a scalable and extensible platform which provides common architecture for data mining and machine learning algorithms of various types.

• Naming of numerous classes/methods/fields changed to be more accurate and comprehensible
• Weka and Rseslib libraries updated to the newest versions: Weka 3.6.1 & Rseslib 3.0.1. Debellor's wrappers adapted
• New class: CrossValidation - evaluator of trainable cells through cross-validation
• New class: RMSE - calculation of Root Mean Squared Error score
• Data objects can be compared and used in collections
• ArffReader can read from a user-provided java.io.InputStream
• More convenient use of parameters (setting values)
• More convenient use of data objects and data types (construction, type casting)
• Other minor improvements to existing classes
• Javadoc extended