step with existing MLLib code), but I can say I was
>
> running
>
> it successfully on quite large data sets.
>
> RJ, depending on where you are in your progress, I'd be happy to help
>
> work
>
> on this piece and / or have you use this as a jumping off point, if
&g
ns Hector points out).
>>>
>>> This needs to be cleaned up, and can surely be optimized (esp. by
>> replacing
>>> the core KMeans step with existing MLLib code), but I can say I was
>> running
>>> it successfully on quite large data sets.
>>&
single-link method with LSH.
> https://issues.apache.org/jira/browse/SPARK-2966
>
> If you have designed the standardized clustering algorithms API, please let
> me know.
>
>
> best,
> Yu Ishikawa
>
>
>
> --
> View this message in context:
> http://apache-
, please let
me know.
best,
Yu Ishikawa
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ng on where you are in your progress, I'd be happy to help
> work
> > on this piece and / or have you use this as a jumping off point, if
> useful.
> >
> > -- Jeremy
> >
> >
> >
> > --
> > View this message in context:
> http://apache-
Hi RJ, that sounds like a great idea. I'd be happy to look over what you put
together.
-- Jeremy
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ppy to help work
> on this piece and / or have you use this as a jumping off point, if useful.
>
> -- Jeremy
>
>
>
> --
> View this message in context:
> http://apache-spark-developers-list.1001551.n3.nabble.com/Contributing-to-MLlib-Proposal-for-Clustering-Algo
e happy to help work
on this piece and / or have you use this as a jumping off point, if useful.
-- Jeremy
--
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Might be worth checking out scikit-learn and mahout to get some broad ideas—
Sent from Mailbox
On Thu, Jul 10, 2014 at 4:25 PM, RJ Nowling wrote:
> I went ahead and created JIRAs.
> JIRA for Hierarchical Clustering:
> https://issues.apache.org/jira/browse/SPARK-2429
> JIRA for Standarized Cluste
I went ahead and created JIRAs.
JIRA for Hierarchical Clustering:
https://issues.apache.org/jira/browse/SPARK-2429
JIRA for Standarized Clustering APIs:
https://issues.apache.org/jira/browse/SPARK-2430
Before submitting a PR for the standardized API, I want to implement a
few clustering algorith
Cool seems like a god initiative. Adding a couple extra high quality clustering
implantations will be great.
I'd say it would make most sense to submit a PR for the Standardised API first,
agree that with everyone and then build on it for the specific implementations.
—
Sent from Mailbox
On We
Thanks everyone for the input.
So it seems what people want is:
* Implement MiniBatch KMeans and Hierarchical KMeans (Divide and
conquer approach, look at DecisionTree implementation as a reference)
* Restructure 3 Kmeans clustering algorithm implementations to prevent
code duplication and confor
Yeah if one were to replace the objective function in decision tree with
minimizing the variance of the leaf nodes it would be a hierarchical
clusterer.
On Tue, Jul 8, 2014 at 2:12 PM, Evan R. Sparks
wrote:
> If you're thinking along these lines, have a look at the DecisionTree
> implementation
If you're thinking along these lines, have a look at the DecisionTree
implementation in MLlib. It uses the same idea and is optimized to prevent
multiple passes over the data by computing several splits at each level of
tree building. The tradeoff is increased model state and computation per
pass o
No was thinking more top-down:
assuming a distributed kmeans system already existing, recursively apply
the kmeans algorithm on data already partitioned by the previous level of
kmeans.
I haven't been much of a fan of bottom up approaches like HAC mainly
because they assume there is already a dis
K doesn't matter much I've tried anything from 2^10 to 10^3 and the
performance
doesn't change much as measured by precision @ K. (see table 1
http://machinelearning.wustl.edu/mlpapers/papers/weston13). Although 10^3
kmeans did outperform 2^10 hierarchical SVD slightly in terms of the
metrics, 2^10
The scikit-learn implementation may be of interest:
http://scikit-learn.org/stable/modules/generated/sklearn.cluster.Ward.html#sklearn.cluster.Ward
It's a bottom up approach. The pair of clusters for merging are
chosen to minimize variance.
Their code is under a BSD license so it can be used as
sure. more interesting problem here is choosing k at each level. Kernel
methods seem to be most promising.
On Tue, Jul 8, 2014 at 1:31 PM, Hector Yee wrote:
> No idea, never looked it up. Always just implemented it as doing k-means
> again on each cluster.
>
> FWIW standard k-means with euclide
No idea, never looked it up. Always just implemented it as doing k-means
again on each cluster.
FWIW standard k-means with euclidean distance has problems too with some
dimensionality reduction methods. Swapping out the distance metric with
negative dot or cosine may help.
Other more useful clust
Hector, could you share the references for hierarchical K-means? thanks.
On Tue, Jul 8, 2014 at 1:01 PM, Hector Yee wrote:
> I would say for bigdata applications the most useful would be hierarchical
> k-means with back tracking and the ability to support k nearest centroids.
>
>
> On Tue, Jul
Having a common framework for clustering makes sense to me. While we
should be careful about what algorithms we include, having solid
implementations of minibatch clustering and hierarchical clustering seems
like a worthwhile goal, and we should reuse as much code and APIs as
reasonable.
On Tue,
Thanks, Hector! Your feedback is useful.
On Tuesday, July 8, 2014, Hector Yee wrote:
> I would say for bigdata applications the most useful would be hierarchical
> k-means with back tracking and the ability to support k nearest centroids.
>
>
> On Tue, Jul 8, 2014 at 10:54 AM, RJ Nowling > wrot
I would say for bigdata applications the most useful would be hierarchical
k-means with back tracking and the ability to support k nearest centroids.
On Tue, Jul 8, 2014 at 10:54 AM, RJ Nowling wrote:
> Hi all,
>
> MLlib currently has one clustering algorithm implementation, KMeans.
> It would
Hi all,
MLlib currently has one clustering algorithm implementation, KMeans.
It would benefit from having implementations of other clustering
algorithms such as MiniBatch KMeans, Fuzzy C-Means, Hierarchical
Clustering, and Affinity Propagation.
I recently submitted a PR [1] for a MiniBatch KMeans
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