Not if you are doing Euclidean distance. On Wed, Jul 20, 2011 at 5:41 AM, Grant Ingersoll (JIRA) <[email protected]>wrote:
> > [ > https://issues.apache.org/jira/browse/MAHOUT-767?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13068331#comment-13068331] > > Grant Ingersoll commented on MAHOUT-767: > ---------------------------------------- > > Why do we need the norms? Why can't we assume the user has already > normalized? > > > Improve RowSimilarityJob performance for count-based distance measures > > ---------------------------------------------------------------------- > > > > Key: MAHOUT-767 > > URL: https://issues.apache.org/jira/browse/MAHOUT-767 > > Project: Mahout > > Issue Type: Improvement > > Reporter: Grant Ingersoll > > Fix For: 0.6 > > > > > > (See > http://www.lucidimagination.com/search/document/40c4f124795c6b5/rowsimilarity_s#42ab816c27c6a9e7for > background) > > Currently, the RowSimilarityJob defers the calculation of the similarity > metric until the reduce phase, while emitting many Cooccurrence objects. > For similarity metrics that are algebraic ( > http://pig.apache.org/docs/r0.8.1/udf.html#Aggregate+Functions) we should > be able to do much of the computation during the Mapper part of this phase > and also take advantage of a Combiner. > > We should use a marker interface to know whether a similarity metric is > algebraic and then make use of an appropriate Mapper implementation, > otherwise we can fall back on our existing implementation. > > -- > This message is automatically generated by JIRA. > For more information on JIRA, see: http://www.atlassian.com/software/jira > > >
