Release Notes

1 February 2014 - Apache Mahout 0.9 released

Highlights include:

Changes in 0.9 are detailed here.

25 July 2013 - Apache Mahout 0.8 released

Highlights include:

  • Numerous performance improvements to Vector and Matrix implementations, API's and their iterators
  • Numerous performance improvements to the recommender implementations
  • MAHOUT-1088: Support for biased item-based recommender
  • MAHOUT-1089: SGD matrix factorization for rating prediction with user and item biases
  • MAHOUT-1106: Support for SVD++
  • MAHOUT-944: Support for converting one or more Lucene storage indexes to SequenceFiles as well as an upgrade of the supported Lucene version to Lucene 4.3.1.
  • MAHOUT-1154 and friends: New streaming k-means implementation that offers on-line (and fast) clustering
  • MAHOUT-833: Make conversion to SequenceFiles Map-Reduce, 'seqdirectory' can now be run as a MapReduce job.
  • MAHOUT-1052: Add an option to MinHashDriver that specifies the dimension of vector to hash (indexes or values).
  • MAHOUT-884: Matrix Concat utility, presently only concatenates two matrices.
  • MAHOUT-1187: Upgraded to CommonsLang3
  • MAHOUT-916: Speedup the Mahout build by making tests run in parallel.

Changes in 0.8 are detailed here.

16 June 2012 - Apache Mahout 0.7 released

Highlights include:

  • Outlier removal capability in K-Means, Fuzzy K, Canopy and Dirichlet Clustering
  • New Clustering implementation for K-Means, Fuzzy K, Canopy and Dirichlet using Cluster Classifiers
  • Collections and Math API consolidated
  • (Complementary) Naive Bayes refactored and cleaned
  • Watchmaker and Old Naive Bayes dropped.
  • Many bug fixes, refactorings, and other small improvements

Changes in 0.7 are detailed here.

6 Feb 2012 - Apache Mahout 0.6 released

Highlights include:

  • Improved Decision Tree performance and added support for regression problems
  • New LDA implementation using Collapsed Variational Bayes 0th Derivative Approximation
  • Reduced runtime of LanczosSolver tests
  • K-Trusses, Top-Down and Bottom-Up clustering, Random Walk with Restarts implementation
  • Reduced runtime of dot product between vectors
  • Added MongoDB and Cassandra DataModel support
  • Increased efficiency of parallel ALS matrix factorization
  • SSVD enhancements
  • Performance improvements in RowSimilarityJob, TransposeJob
  • Added numerous clustering display examples
  • Many bug fixes, refactorings, and other small improvements

Changes in 0.6 are detailed here.

Past Releases