About: MyMediaLite is a lightweight, multi-purpose library of recommender system algorithms.

Important changes:

• new rating predictor GSVD++ (contributed by Marcelo Manzato)
• new recommenders ExternalRatingPredictor and ExternalItemRecommender to evaluate external tools with the MyMediaLite evaluation framework
• incremental update support for item recommendation UserKNN and ItemKNN (based on a pull request by João Vinagre)
• --cross-validation support for the rating_based_ranking tool (as requested by Pieter-Jan Verbrugen)
• removed the group recommendation code
• cleaner item recommendation evaluation, with a bug fix in the cross-validation code and a complete rewrite of online evaluation
• removed unused matrix and vector math, faster and simplified matrix code

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: A set of Perl programs for generating and manipulating ROC curves.

Initial Announcement on mloss.org.

About: Given many points in ROC (Receiver Operator Characteristics) space, computes the convex hull.

Initial Announcement on mloss.org.