context:
http://lucene.472066.n3.nabble.com/What-are-the-best-settings-for-my-clustering-task-tp4092807.html
Sent from the Mahout User List mailing list archive at Nabble.com.
--
> View this message in context:
> http://lucene.472066.n3.nabble.com/What-are-the-best-settings-for-my-clustering-task-tp4092807.html
> Sent from the Mahout User List mailing list archive at Nabble.com.
>
; the
> > cluster count.
> >
> > The question is: what do I need to tweak with regard to the available
> > mahout
> > settings, so the clusters are created as precisely as possible?
> >
> > Many regards!
> > Jens
> >
> >
> >
> >
t;
> > > I adapted the reuters cluster script to read in my data and managed to
> > > create a first set of clusters. However, I have not managed to maximise
> > the
> > > cluster count.
> > >
> > > The question is: what do I need to tweak with regard to t
mound of found
> clusters
> > > (the
> > > > best possible value would be 5.000 clusters with 10 products each)
> > > >
> > > > I adapted the reuters cluster script to read in my data and managed
> to
> > > > create a first set of cluste
; > > > > What would be a good approach to maximise the amound of found
> > clusters
> > > > (the
> > > > > best possible value would be 5.000 clusters with 10 products each)
> > > > >
> > > > > I adapted the reuters cluste
> > > ten
> > > > > > varying product descriptions per product. The product
> descriptions
> > > are
> > > > > > already prepared for clustering and contain a normalized brand
> > name,
> > > > > > product
> &g
and
> > > > > ten
> > > > > > varying product descriptions per product. The product
> descriptions
> > > are
> > > > > > already prepared for clustering and contain a normalized brand
> > name,
> > > > > > pr
30, 2013 at 2:14 PM, mercutio7979 <
>>> jbon...@googlemail.com
>>>>>>> wrote:
>>>>>>
>>>>>>> Hello all,
>>>>>>>
>>>>>>> I am currently trying create clusters fr
? What else could I
> >> use?
> >>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>> 2013/10/1 Ted Dunning
> >>>>>
> >>>>>> At such small sizes, I would guess that the sequential version of
> >> the
> >>>>>> streaming k-means or ball k-means would be better options.
> >>>>>>
> >>>>>>
> >>>>>>
> >>>>>> On Mon, Sep 30, 2013 at 2:14 PM, mercutio7979 <
> >>> jbon...@googlemail.com
> >>>>>>> wrote:
> >>>>>>
> >>>>>>> Hello all,
> >>>>>>>
> >>>>>>> I am currently trying create clusters from a group of 50.000
> >>> strings
> >>>>> that
> >>>>>>> contain product descriptions (around 70-100 characters length
> >>> each).
> >>>>>>>
> >>>>>>> That group of 50.000 consists of roughly 5.000 individual
> >> products
> >>>> and
> >>>>>> ten
> >>>>>>> varying product descriptions per product. The product
> >> descriptions
> >>>> are
> >>>>>>> already prepared for clustering and contain a normalized brand
> >>> name,
> >>>>>>> product
> >>>>>>> model number, etc.
> >>>>>>>
> >>>>>>> What would be a good approach to maximise the amound of found
> >>>> clusters
> >>>>>> (the
> >>>>>>> best possible value would be 5.000 clusters with 10 products
> >> each)
> >>>>>>>
> >>>>>>> I adapted the reuters cluster script to read in my data and
> >> managed
> >>>> to
> >>>>>>> create a first set of clusters. However, I have not managed to
> >>>> maximise
> >>>>>> the
> >>>>>>> cluster count.
> >>>>>>>
> >>>>>>> The question is: what do I need to tweak with regard to the
> >>> available
> >>>>>>> mahout
> >>>>>>> settings, so the clusters are created as precisely as possible?
> >>>>>>>
> >>>>>>> Many regards!
> >>>>>>> Jens
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>>
> >>>>>>> --
> >>>>>>> View this message in context:
> >>>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>>
> >>
> http://lucene.472066.n3.nabble.com/What-are-the-best-settings-for-my-clustering-task-tp4092807.html
> >>>>>>> Sent from the Mahout User List mailing list archive at
> >> Nabble.com.
> >>>>>>>
> >>>>>>
> >>>>>
> >>>>
> >>>
> >>>
rithm.
>>>>>>>
>>>>>>> According to that paper, k-means relies on a preknown/predefined
>>>> amount
>>>>>> of
>>>>>>> clusters as an input parameter.
>>>>>>>
>>>>>>
gt;> Isn't the streaming k-means just a different approach to crunch
> >>>> through
> >>>>>> the
> >>>>>>> data? In other words, the result of streaming k-means should be
> >>>>>> comparable
> >>>>>>> to using k-means in multiple chained map reduce cycl
12 matches
Mail list logo