Re: Kmeans clusterdump Interpretation
Oh, I thought kmeans gave me a point vector as a centroid, not a calculated
point central to a cluster. I guess in this case I would be looking for the
most central point vector (from the index ) that I can use as a
representative of the cluster.
On Tue, Jul 21, 2015 at 6:41 AM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
I'm not sure centroid id is even a defined thing, especially since the
centroid, in my understanding, is just a point in space, not necessarily a
point in your data.
Are you trying to find the most-central point in a given cluster?
On Mon, Jul 20, 2015 at 5:18 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
Hi,
I've been messing with mahout 0.10 and kmeans clustering with a solr
4.6.1
index. The data is news articles. The --field option for kmeans is set to
content. The idField is set to title (just so i can analyse it
faster).
The clusterdump of the kmeans result gives me a proper output, but I cant
figure out the id of the vector chosen as the center. There are only
14-15
articles so I am not hung up about the cluster performance at this time.
I used random seeds for the kmeans commandline.
For reference, this is the commandline cluster dump I am executing
bin/mahout clusterdump -i $MAHOUT_HOME/testCluster/clusters-3-final
-p $MAHOUT_HOME/testCluster/clusteredPoints -d $MAHOUT_HOME/dict.txt -b 5
The output I get is off the form
:{r:
top terms
x==x
Weight : [props - optional]: Point:
1.0 : [distance=0.0]: [{account:0.026}...other features]
1.0 : [distance=0.3963903651622338]: []
So how exactly do I get the centroid id? I have even tried accessing it
with java
ClusterWritable value.getValue().getCenter() but this just gives me the
features and values of the centroid.
Also, please do explain the meaning of account:0.026 (just making sure
I
know it right). I used tfidf.
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
Re: Kmeans clusterdump Interpretation
The most central point in a cluster is often referred to as a medoid
(similar to median, but multi-dimensional).
The Mahout code does not compute medoids. In general, they are difficult
to compute and implementing a full k-medoid clustering algorithm even more
so.
On Mon, Jul 20, 2015 at 6:25 PM, Ankit Goel ankitgoel2...@gmail.com wrote:
Oh, I thought kmeans gave me a point vector as a centroid, not a calculated
point central to a cluster. I guess in this case I would be looking for the
most central point vector (from the index ) that I can use as a
representative of the cluster.
On Tue, Jul 21, 2015 at 6:41 AM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
I'm not sure centroid id is even a defined thing, especially since the
centroid, in my understanding, is just a point in space, not necessarily
a
point in your data.
Are you trying to find the most-central point in a given cluster?
On Mon, Jul 20, 2015 at 5:18 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
Hi,
I've been messing with mahout 0.10 and kmeans clustering with a solr
4.6.1
index. The data is news articles. The --field option for kmeans is set
to
content. The idField is set to title (just so i can analyse it
faster).
The clusterdump of the kmeans result gives me a proper output, but I
cant
figure out the id of the vector chosen as the center. There are only
14-15
articles so I am not hung up about the cluster performance at this
time.
I used random seeds for the kmeans commandline.
For reference, this is the commandline cluster dump I am executing
bin/mahout clusterdump -i $MAHOUT_HOME/testCluster/clusters-3-final
-p $MAHOUT_HOME/testCluster/clusteredPoints -d $MAHOUT_HOME/dict.txt
-b 5
The output I get is off the form
:{r:
top terms
x==x
Weight : [props - optional]: Point:
1.0 : [distance=0.0]: [{account:0.026}...other features]
1.0 : [distance=0.3963903651622338]: []
So how exactly do I get the centroid id? I have even tried accessing it
with java
ClusterWritable value.getValue().getCenter() but this just gives me the
features and values of the centroid.
Also, please do explain the meaning of account:0.026 (just making
sure
I
know it right). I used tfidf.
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
Re: Kmeans clusterdump Interpretation
That kind of puts me in a tough position. I was planning to use kmeans as a
method for aggregating similar articles from multiple news sources, and
then getting a representative article from those. Here I mean similar as in
the articles are from different news sources but are about the exact same
thing. Intuitively it seems that these articles would get grouped
together. Any suggestions how I should go about that? So far I'm using
nutch to crawl, solr to index and now I'm here on mahout.
On Tue, Jul 21, 2015 at 7:10 AM, Ted Dunning ted.dunn...@gmail.com wrote:
The most central point in a cluster is often referred to as a medoid
(similar to median, but multi-dimensional).
The Mahout code does not compute medoids. In general, they are difficult
to compute and implementing a full k-medoid clustering algorithm even more
so.
On Mon, Jul 20, 2015 at 6:25 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
Oh, I thought kmeans gave me a point vector as a centroid, not a
calculated
point central to a cluster. I guess in this case I would be looking for
the
most central point vector (from the index ) that I can use as a
representative of the cluster.
On Tue, Jul 21, 2015 at 6:41 AM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
I'm not sure centroid id is even a defined thing, especially since the
centroid, in my understanding, is just a point in space, not
necessarily
a
point in your data.
Are you trying to find the most-central point in a given cluster?
On Mon, Jul 20, 2015 at 5:18 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
Hi,
I've been messing with mahout 0.10 and kmeans clustering with a solr
4.6.1
index. The data is news articles. The --field option for kmeans is
set
to
content. The idField is set to title (just so i can analyse it
faster).
The clusterdump of the kmeans result gives me a proper output, but I
cant
figure out the id of the vector chosen as the center. There are only
14-15
articles so I am not hung up about the cluster performance at this
time.
I used random seeds for the kmeans commandline.
For reference, this is the commandline cluster dump I am executing
bin/mahout clusterdump -i $MAHOUT_HOME/testCluster/clusters-3-final
-p $MAHOUT_HOME/testCluster/clusteredPoints -d $MAHOUT_HOME/dict.txt
-b 5
The output I get is off the form
:{r:
top terms
x==x
Weight : [props - optional]: Point:
1.0 : [distance=0.0]: [{account:0.026}...other features]
1.0 : [distance=0.3963903651622338]: []
So how exactly do I get the centroid id? I have even tried accessing
it
with java
ClusterWritable value.getValue().getCenter() but this just gives me
the
features and values of the centroid.
Also, please do explain the meaning of account:0.026 (just making
sure
I
know it right). I used tfidf.
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
Re: Kmeans clusterdump Interpretation
It's possible you could write a post-processing step to find the closest
point to the centroid based on the distance property if I'm recalling it
correctly.
On Mon, Jul 20, 2015 at 6:45 PM, Ankit Goel ankitgoel2...@gmail.com wrote:
That kind of puts me in a tough position. I was planning to use kmeans as a
method for aggregating similar articles from multiple news sources, and
then getting a representative article from those. Here I mean similar as in
the articles are from different news sources but are about the exact same
thing. Intuitively it seems that these articles would get grouped
together. Any suggestions how I should go about that? So far I'm using
nutch to crawl, solr to index and now I'm here on mahout.
On Tue, Jul 21, 2015 at 7:10 AM, Ted Dunning ted.dunn...@gmail.com
wrote:
The most central point in a cluster is often referred to as a medoid
(similar to median, but multi-dimensional).
The Mahout code does not compute medoids. In general, they are difficult
to compute and implementing a full k-medoid clustering algorithm even
more
so.
On Mon, Jul 20, 2015 at 6:25 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
Oh, I thought kmeans gave me a point vector as a centroid, not a
calculated
point central to a cluster. I guess in this case I would be looking for
the
most central point vector (from the index ) that I can use as a
representative of the cluster.
On Tue, Jul 21, 2015 at 6:41 AM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
I'm not sure centroid id is even a defined thing, especially since
the
centroid, in my understanding, is just a point in space, not
necessarily
a
point in your data.
Are you trying to find the most-central point in a given cluster?
On Mon, Jul 20, 2015 at 5:18 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
Hi,
I've been messing with mahout 0.10 and kmeans clustering with a
solr
4.6.1
index. The data is news articles. The --field option for kmeans is
set
to
content. The idField is set to title (just so i can analyse it
faster).
The clusterdump of the kmeans result gives me a proper output, but
I
cant
figure out the id of the vector chosen as the center. There are
only
14-15
articles so I am not hung up about the cluster performance at this
time.
I used random seeds for the kmeans commandline.
For reference, this is the commandline cluster dump I am executing
bin/mahout clusterdump -i $MAHOUT_HOME/testCluster/clusters-3-final
-p $MAHOUT_HOME/testCluster/clusteredPoints -d
$MAHOUT_HOME/dict.txt
-b 5
The output I get is off the form
:{r:
top terms
x==x
Weight : [props - optional]: Point:
1.0 : [distance=0.0]: [{account:0.026}...other features]
1.0 : [distance=0.3963903651622338]: []
So how exactly do I get the centroid id? I have even tried
accessing
it
with java
ClusterWritable value.getValue().getCenter() but this just gives me
the
features and values of the centroid.
Also, please do explain the meaning of account:0.026 (just making
sure
I
know it right). I used tfidf.
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
Re: Kmeans clusterdump Interpretation
I'm not sure centroid id is even a defined thing, especially since the
centroid, in my understanding, is just a point in space, not necessarily a
point in your data.
Are you trying to find the most-central point in a given cluster?
On Mon, Jul 20, 2015 at 5:18 PM, Ankit Goel ankitgoel2...@gmail.com wrote:
Hi,
I've been messing with mahout 0.10 and kmeans clustering with a solr 4.6.1
index. The data is news articles. The --field option for kmeans is set to
content. The idField is set to title (just so i can analyse it faster).
The clusterdump of the kmeans result gives me a proper output, but I cant
figure out the id of the vector chosen as the center. There are only 14-15
articles so I am not hung up about the cluster performance at this time.
I used random seeds for the kmeans commandline.
For reference, this is the commandline cluster dump I am executing
bin/mahout clusterdump -i $MAHOUT_HOME/testCluster/clusters-3-final
-p $MAHOUT_HOME/testCluster/clusteredPoints -d $MAHOUT_HOME/dict.txt -b 5
The output I get is off the form
:{r:
top terms
x==x
Weight : [props - optional]: Point:
1.0 : [distance=0.0]: [{account:0.026}...other features]
1.0 : [distance=0.3963903651622338]: []
So how exactly do I get the centroid id? I have even tried accessing it
with java
ClusterWritable value.getValue().getCenter() but this just gives me the
features and values of the centroid.
Also, please do explain the meaning of account:0.026 (just making sure I
know it right). I used tfidf.
--
Regards,
Ankit Goel
http://about.me/ankitgoel
Re: Kmeans clusterdump Interpretation
Hmm, kmeans algorithmically is supposed to only annoint existing
vectors(documents) as the centroid for a cluster every step (or so I
believe). If mahout is generating non document vector as a centroid, it
changes a lot of things.
That would also explain the -distanceMeasure option in clusterdump. As
Andrew mentions, running clusterdump with the default euclidean measure
should give me the closest document vector to the calculated centroid.
Please correct me if I'm wrong anywhere.
Thanks
On Tue, Jul 21, 2015 at 7:33 AM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
It's possible you could write a post-processing step to find the closest
point to the centroid based on the distance property if I'm recalling it
correctly.
On Mon, Jul 20, 2015 at 6:45 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
That kind of puts me in a tough position. I was planning to use kmeans
as a
method for aggregating similar articles from multiple news sources, and
then getting a representative article from those. Here I mean similar as
in
the articles are from different news sources but are about the exact same
thing. Intuitively it seems that these articles would get grouped
together. Any suggestions how I should go about that? So far I'm using
nutch to crawl, solr to index and now I'm here on mahout.
On Tue, Jul 21, 2015 at 7:10 AM, Ted Dunning ted.dunn...@gmail.com
wrote:
The most central point in a cluster is often referred to as a medoid
(similar to median, but multi-dimensional).
The Mahout code does not compute medoids. In general, they are
difficult
to compute and implementing a full k-medoid clustering algorithm even
more
so.
On Mon, Jul 20, 2015 at 6:25 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
Oh, I thought kmeans gave me a point vector as a centroid, not a
calculated
point central to a cluster. I guess in this case I would be looking
for
the
most central point vector (from the index ) that I can use as a
representative of the cluster.
On Tue, Jul 21, 2015 at 6:41 AM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
I'm not sure centroid id is even a defined thing, especially since
the
centroid, in my understanding, is just a point in space, not
necessarily
a
point in your data.
Are you trying to find the most-central point in a given cluster?
On Mon, Jul 20, 2015 at 5:18 PM, Ankit Goel
ankitgoel2...@gmail.com
wrote:
Hi,
I've been messing with mahout 0.10 and kmeans clustering with a
solr
4.6.1
index. The data is news articles. The --field option for kmeans
is
set
to
content. The idField is set to title (just so i can analyse
it
faster).
The clusterdump of the kmeans result gives me a proper output,
but
I
cant
figure out the id of the vector chosen as the center. There are
only
14-15
articles so I am not hung up about the cluster performance at
this
time.
I used random seeds for the kmeans commandline.
For reference, this is the commandline cluster dump I am
executing
bin/mahout clusterdump -i
$MAHOUT_HOME/testCluster/clusters-3-final
-p $MAHOUT_HOME/testCluster/clusteredPoints -d
$MAHOUT_HOME/dict.txt
-b 5
The output I get is off the form
:{r:
top terms
x==x
Weight : [props - optional]: Point:
1.0 : [distance=0.0]: [{account:0.026}...other features]
1.0 : [distance=0.3963903651622338]: []
So how exactly do I get the centroid id? I have even tried
accessing
it
with java
ClusterWritable value.getValue().getCenter() but this just gives
me
the
features and values of the centroid.
Also, please do explain the meaning of account:0.026 (just
making
sure
I
know it right). I used tfidf.
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
Re: Kmeans clusterdump Interpretation
You can always just pick the article closest to the centroid.
But I think that you may find that with simple k-means that clusters are
going to be about more than one thing.
On Mon, Jul 20, 2015 at 8:21 PM, Ankit Goel ankitgoel2...@gmail.com wrote:
Hmm, kmeans algorithmically is supposed to only annoint existing
vectors(documents) as the centroid for a cluster every step (or so I
believe). If mahout is generating non document vector as a centroid, it
changes a lot of things.
That would also explain the -distanceMeasure option in clusterdump. As
Andrew mentions, running clusterdump with the default euclidean measure
should give me the closest document vector to the calculated centroid.
Please correct me if I'm wrong anywhere.
Thanks
On Tue, Jul 21, 2015 at 7:33 AM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
It's possible you could write a post-processing step to find the closest
point to the centroid based on the distance property if I'm recalling
it
correctly.
On Mon, Jul 20, 2015 at 6:45 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
That kind of puts me in a tough position. I was planning to use kmeans
as a
method for aggregating similar articles from multiple news sources, and
then getting a representative article from those. Here I mean similar
as
in
the articles are from different news sources but are about the exact
same
thing. Intuitively it seems that these articles would get grouped
together. Any suggestions how I should go about that? So far I'm using
nutch to crawl, solr to index and now I'm here on mahout.
On Tue, Jul 21, 2015 at 7:10 AM, Ted Dunning ted.dunn...@gmail.com
wrote:
The most central point in a cluster is often referred to as a medoid
(similar to median, but multi-dimensional).
The Mahout code does not compute medoids. In general, they are
difficult
to compute and implementing a full k-medoid clustering algorithm even
more
so.
On Mon, Jul 20, 2015 at 6:25 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
Oh, I thought kmeans gave me a point vector as a centroid, not a
calculated
point central to a cluster. I guess in this case I would be looking
for
the
most central point vector (from the index ) that I can use as a
representative of the cluster.
On Tue, Jul 21, 2015 at 6:41 AM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
I'm not sure centroid id is even a defined thing, especially
since
the
centroid, in my understanding, is just a point in space, not
necessarily
a
point in your data.
Are you trying to find the most-central point in a given cluster?
On Mon, Jul 20, 2015 at 5:18 PM, Ankit Goel
ankitgoel2...@gmail.com
wrote:
Hi,
I've been messing with mahout 0.10 and kmeans clustering with a
solr
4.6.1
index. The data is news articles. The --field option for kmeans
is
set
to
content. The idField is set to title (just so i can analyse
it
faster).
The clusterdump of the kmeans result gives me a proper output,
but
I
cant
figure out the id of the vector chosen as the center. There are
only
14-15
articles so I am not hung up about the cluster performance at
this
time.
I used random seeds for the kmeans commandline.
For reference, this is the commandline cluster dump I am
executing
bin/mahout clusterdump -i
$MAHOUT_HOME/testCluster/clusters-3-final
-p $MAHOUT_HOME/testCluster/clusteredPoints -d
$MAHOUT_HOME/dict.txt
-b 5
The output I get is off the form
:{r:
top terms
x==x
Weight : [props - optional]: Point:
1.0 : [distance=0.0]: [{account:0.026}...other features]
1.0 : [distance=0.3963903651622338]: []
So how exactly do I get the centroid id? I have even tried
accessing
it
with java
ClusterWritable value.getValue().getCenter() but this just
gives
me
the
features and values of the centroid.
Also, please do explain the meaning of account:0.026 (just
making
sure
I
know it right). I used tfidf.
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
Re: Kmeans clusterdump Interpretation
True that. Kmeans is just a first step anyways. Definetely needs tuning.
Thanks guys
On Tue, Jul 21, 2015 at 9:46 AM, Ted Dunning ted.dunn...@gmail.com wrote:
You can always just pick the article closest to the centroid.
But I think that you may find that with simple k-means that clusters are
going to be about more than one thing.
On Mon, Jul 20, 2015 at 8:21 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
Hmm, kmeans algorithmically is supposed to only annoint existing
vectors(documents) as the centroid for a cluster every step (or so I
believe). If mahout is generating non document vector as a centroid, it
changes a lot of things.
That would also explain the -distanceMeasure option in clusterdump. As
Andrew mentions, running clusterdump with the default euclidean measure
should give me the closest document vector to the calculated centroid.
Please correct me if I'm wrong anywhere.
Thanks
On Tue, Jul 21, 2015 at 7:33 AM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
It's possible you could write a post-processing step to find the
closest
point to the centroid based on the distance property if I'm recalling
it
correctly.
On Mon, Jul 20, 2015 at 6:45 PM, Ankit Goel ankitgoel2...@gmail.com
wrote:
That kind of puts me in a tough position. I was planning to use
kmeans
as a
method for aggregating similar articles from multiple news sources,
and
then getting a representative article from those. Here I mean similar
as
in
the articles are from different news sources but are about the exact
same
thing. Intuitively it seems that these articles would get grouped
together. Any suggestions how I should go about that? So far I'm
using
nutch to crawl, solr to index and now I'm here on mahout.
On Tue, Jul 21, 2015 at 7:10 AM, Ted Dunning ted.dunn...@gmail.com
wrote:
The most central point in a cluster is often referred to as a
medoid
(similar to median, but multi-dimensional).
The Mahout code does not compute medoids. In general, they are
difficult
to compute and implementing a full k-medoid clustering algorithm
even
more
so.
On Mon, Jul 20, 2015 at 6:25 PM, Ankit Goel
ankitgoel2...@gmail.com
wrote:
Oh, I thought kmeans gave me a point vector as a centroid, not a
calculated
point central to a cluster. I guess in this case I would be
looking
for
the
most central point vector (from the index ) that I can use as a
representative of the cluster.
On Tue, Jul 21, 2015 at 6:41 AM, Andrew Musselman
andrew.mussel...@gmail.com wrote:
I'm not sure centroid id is even a defined thing, especially
since
the
centroid, in my understanding, is just a point in space, not
necessarily
a
point in your data.
Are you trying to find the most-central point in a given
cluster?
On Mon, Jul 20, 2015 at 5:18 PM, Ankit Goel
ankitgoel2...@gmail.com
wrote:
Hi,
I've been messing with mahout 0.10 and kmeans clustering
with a
solr
4.6.1
index. The data is news articles. The --field option for
kmeans
is
set
to
content. The idField is set to title (just so i can
analyse
it
faster).
The clusterdump of the kmeans result gives me a proper
output,
but
I
cant
figure out the id of the vector chosen as the center. There
are
only
14-15
articles so I am not hung up about the cluster performance at
this
time.
I used random seeds for the kmeans commandline.
For reference, this is the commandline cluster dump I am
executing
bin/mahout clusterdump -i
$MAHOUT_HOME/testCluster/clusters-3-final
-p $MAHOUT_HOME/testCluster/clusteredPoints -d
$MAHOUT_HOME/dict.txt
-b 5
The output I get is off the form
:{r:
top terms
x==x
Weight : [props - optional]: Point:
1.0 : [distance=0.0]: [{account:0.026}...other
features]
1.0 : [distance=0.3963903651622338]: []
So how exactly do I get the centroid id? I have even tried
accessing
it
with java
ClusterWritable value.getValue().getCenter() but this just
gives
me
the
features and values of the centroid.
Also, please do explain the meaning of account:0.026 (just
making
sure
I
know it right). I used tfidf.
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
http://about.me/ankitgoel
--
Regards,
Ankit Goel
