As we reported in our previous Digest, Mahout was approved by the board to become Top Level Project (TLP) and TLP-related preparations kept Mahout developers busy in May. Mahout mailing lists (user- and dev-) were moved to their new addresses and all subscribers were automatically moved to the new lists: email@example.com and firstname.lastname@example.org. Regarding other TLP-related changes and their progress, check the list of items to complete the move.
We’ll start a quick walk-through of May’s new features and improvements with an effort on reducing Mahout’s Vector representation size on disk, resulting in improvement of 11% lower size on a test data set.
Discussion on how UncenteredCosineSimilarity , an implementation of the cosine similarity which does not “center” its data, should behave in distributed vs. non-distributed version and a patch for distributed version can be found in MAHOUT-389. Furthermore, an implementation of distributed version of any ItemSimilarity currently available in a non-distributed form was committed in MAHOUT-393.
Mahout has utilities to generate Vectors from a directory of text documents and one improvement in terms of consistency and ease of understanding was made on tf/tfidf-vectors outputs. When using bin/mahout seq2sparse command to generate vectors from text (actually, from the sequence file generated from text), depending on the weighting method (term frequency or term frequency–inverse document frequency), output was created in different directories. Now with the fix from MAHOUT-398, tf-vectors and tfidf-vectors output directories at the same level.
We’ll end with Hidden Markov Model and its integration into Mahout. In MAHOUT-396 you’ll find a patch and more information regarding where and how it is used.
That’s all from Mahout’s world for May, please feel free to leave any questions or comments and don’t forget you can follow @sematext on Twitter.