A better approach I can think of for the aformentioned task is to use Latent
Dirichlet Allocation
You can force, LDA to learn topics with certain specific words by assigning
higher probability values to those words in the initial dirichlet distribution.
That way you will be able to discover
A work around would be to bin the data, that is divide it into some range lets
say 0-5, 5-10,10-15
and consider each range to be a feature.
If the given value corresponds to a range, the value for that feature would be
1.
A better solution is to use something like SVM, that can be used much
From: Ted Dunning ted.dunn...@gmail.com
Sent: Sunday, November 30, 2014 6:29 PM
To: user@mahout.apache.org
Subject: Re: DBSCAN implementation in Mahout
On Sat, Nov 29, 2014 at 8:31 PM, 3316 Chirag Nagpal
chiragnagpal_12...@aitpune.edu.in wrote:
Since Density based clustering algorithms
, November 29, 2014 11:29 PM
To: user@mahout.apache.org
Subject: Re: DBSCAN implementation in Mahout
No there is no dbscan, optics or any other density flavor afaik
Sent from my phone.
On Nov 28, 2014 11:41 AM, 3316 Chirag Nagpal
chiragnagpal_12...@aitpune.edu.in wrote:
?
Hello
I am Chirag Nagpal
?
Hello
I am Chirag Nagpal, a third year student of Computer Engineering at the
University of Pune, India and currently interning at SERC, Indian Institute of
Science, Bangalore
My work involves using density based clustering algorithms like DBSCAN on
geo-referenced data like Tweets.
