This code is putting everything in points ( which I think is some sort
of collection ). This will obviously throw OOM for large files.
The vectors should be added to a sequence file and then the path to
that sequence file should be given as input to the clustering algorithm.
Mahout in action has a code snippet which does it. Googling "writing
into a hdfs sequence file" would also help.
On 19-09-2012 16:06, Rahul Mishra wrote:
For small file it works absolutely fine. But, I get this error for large
files :
Exception in thread "main" java.lang.OutOfMemoryError: GC overhead limit
exceeded
Initially, I am reading csv file using the following code and I presume,
the issue is here. Kindly suggest better approach.
CSVReader reader = new CSVReader(new FileReader(inputPath));
double field = -1;
int lineCount = 0;
String [] nextLine;
while ((nextLine = reader.readNext()) != null) {
lineCount++;
//ArrayList<Double> attributes = new ArrayList<Double>();
double[] d_attrib = new double[4];
for(int i=0;i<nextLine.length;i++)
{
d_attrib[i] = Double.parseDouble(nextLine[i]);
// attributes.add(Double.parseDouble(nextLine[i]));
}
//Double[] d_attrib= attributes.toArray(new Double[attributes.size()]);
NamedVector vec = new NamedVector(new
RandomAccessSparseVector(nextLine.length)," " + lineCount+" "); //name the
vector with msisdn
vec.assign(d_attrib);
points.add(vec);
}
On Wed, Sep 19, 2012 at 3:03 PM, Lance Norskog <[email protected]> wrote:
If you have your Hadoop cluster in your environment variables, most Mahout
jobs use the cluster by default. So, if you can run 'hadoop fs' and look at
your hdfs cluster, Mahout should find your Hadoop cluster.
Lance
----- Original Message -----
| From: "Paritosh Ranjan" <[email protected]>
| To: [email protected]
| Sent: Tuesday, September 18, 2012 11:28:28 PM
| Subject: Re: Clustering large files using hadoop?
|
| KMeansDriver has a run method with a flag runSequential. When you
| will
| mark it to false, it will use the hadoop cluster to scale. kmeans
| command is also having this flag.
|
| "
|
| In the process, I have been able to vectorize the data points and
| use the
| clustering results of K-means to feed it as the initial centroid to
| Fuzzy
| K-means clustering.
|
| "
| You can also use Canopy clustering for initial seeding, as its a
| single
| iteration clustering algorithm and produces good results if proper
| t1,t2
| values are provided.
| https://cwiki.apache.org/confluence/display/MAHOUT/Canopy+Clustering
|
|
| On 19-09-2012 11:47, Rahul Mishra wrote:
| > I have been able to cluster and generate results for small csv
| > files(having
| > only continuous values) on a local system using eclipse and it
| > works
| > smoothly.
| > In the process, I have been able to vectorize the data points and
| > use the
| > clustering results of K-means to feed it as the initial centroid to
| > Fuzzy
| > K-means clustering.
| >
| > But, in the end I am able to do it only for small files . For files
| > having
| > 2 million rows, it simply shows error out of memory.
| > But, since Mahout is for large scale machine learning , how do I
| > convert my
| > code to use the power of map-reduce framework of hadoop.[info: I
| > have
| > access to a 3-node Cluster having hadoop]
| > Can anyone suggest a step-by-step procedure?
| >
| > I have also looked into the clustering chapters of the book "Mahout
| > in
| > Action" but to my dismay did not find any clue.
| >
|
|
|
