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.

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.

About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT-based features (i.e. 3D MoSIFT, 3D EMoSIFT and 3D SMoSIFT), and the MFSK feature.

Initial Announcement on mloss.org.

About: Rival is an open source Java toolkit for recommender system evaluation. It provides a simple way to create evaluation results comparable across different recommendation frameworks.

Initial Announcement on mloss.org.

About: Open Source Machine Learning Server

• Single machine version for small-to-medium scale deployments
• Integrated GraphChi (disk-based large-scale graph computation) and algorithms: ALS, CCD++, SGD, CLiMF
• Improved runtime for training and offline evaluation
• Bug fixes

See release notes - https://predictionio.atlassian.net/secure/ReleaseNote.jspa?projectId=10000&version=11801

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.