Hi,
I am currently working on a project that
needs categorization of documents (UN-structured data) based on internal
context of document. I am using Apache mahout clustering solution for the
same. So far we have explored Kmeans, Canopy with Kmeans, We have also used
Lucene analyzer
I have successfully ran CVB JOB with input k (number of topics) as 20 -x
and got the following files as output of the Job
1. CVB output for parameter “*-o*” which is vector file with 20
records.
2. “doc-topic-distributions” for parameter “*-dt*” which is a vector
file with 806 records
i am also stuck at the same stage and could not figure out how to relate
documents to topic distributions .
i saw that the CVB output is a sequence file of vectors as
"org.apache.hadoop.io.IntWritable%org.apache.mahout.math.VectorWritable"
and the topic distribution file is also same
"org.apache
Hi All,
I have been trying mahout clustering on unstructured data i.e human
written data . I have tried mahout clustering algorithms like
Kmeans,Canopy+Kmeans and LDA but the results produced are not help full .
i see the problem is with the way data is written , Can some one please
provide me
nds on the nature of data you are clustering. If you have knowledge
> about your data, you can figure out the results and you can also set the
> correct parameters to the clustering algorithm like number of topics or
> number of clusters.
>
> Cheers,
> Donni
>
> On Thu, Dec 4,
yes downloading the source code and building that in Eclipse will be good
choice for that you need to have maven installed and configured on your
machine . Once you are done you can simply configure the mahout
distribution in you class path to use the classes of mahout and create your
own project t