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https://issues.apache.org/jira/browse/SPARK-3588?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14224287#comment-14224287
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Travis Galoppo commented on SPARK-3588:
---------------------------------------

Sorry about the duplicate effort; I did a search prior to my PR, but somehow 
missed this ticket.  I will gladly coordinate to improve my submission.

cc: [~mengxr] [~MeethuM] 

> Gaussian Mixture Model clustering
> ---------------------------------
>
>                 Key: SPARK-3588
>                 URL: https://issues.apache.org/jira/browse/SPARK-3588
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib, PySpark
>            Reporter: Meethu Mathew
>            Assignee: Meethu Mathew
>         Attachments: GMMSpark.py
>
>
> Gaussian Mixture Models (GMM) is a popular technique for soft clustering. GMM 
> models the entire data set as a finite mixture of Gaussian distributions,each 
> parameterized by a mean vector µ ,a covariance matrix ∑ and  a mixture weight 
> π. In this technique, probability of  each point to belong to each cluster is 
> computed along with the cluster statistics.
> We have come up with an initial distributed implementation of GMM in pyspark 
> where the parameters are estimated using the  Expectation-Maximization 
> algorithm.Our current implementation considers diagonal covariance matrix for 
> each component.
> We did an initial benchmark study on a  2 node Spark standalone cluster setup 
> where each node config is 8 Cores,8 GB RAM, the spark version used is 1.0.0. 
> We also evaluated python version of k-means available in spark on the same 
> datasets.
> Below are the results from this benchmark study. The reported stats are 
> average from 10 runs.Tests were done on multiple datasets with varying number 
> of features and instances.
> ||          Dataset  
>                ||   Gaussian
>  mixture model     || 
>            Kmeans(Python)           ||
>          
> |Instances|Dimensions |Avg time per iteration|Time for  100 iterations |Avg 
> time per iteration |Time for 100 iterations | 
> |0.7million|    13 
>           |  
>            7s 
>             | 
>              12min 
>            |  
>             13s  
>         |          26min 
>        |
> |1.8million|    11 
>           |   
>         17s 
>              | 
>            29min 
>            |  
>             33s  
>          |          53min 
>      |
> |10million|   16 
>           |  
>         1.6min         
>  |            2.7hr 
>              |  
>            1.2min         | 
>          2hr           
>  |



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