<p>At the risk of sounding immodest, I wrote a <a href="http://www.cs.utah.edu/~suresh/web/2008/03/29/clustering-on-streams/">short survey on stream clustering</a> a few years ago. It's a little out of date, but not overly so, and doing forward citations will get you to the recent work in the area. </p>

CC BY-SA 3.0
2013-03-14T09:30:08.503Z
Thank you. I can see that people are very interested in streams. In my problem I have access to all the data, it is just very large.
Majid
CC BY-SA 3.0
2013-03-08T00:17:07.113Z
2013-03-08T00:17:07.113Z
Suresh Venkat
<p>If you have a very large data set of $n$ vectors and you want to cluster them according to some metric measure, what is the current state of the art when you can not afford to do more than $\Theta(n)$ work?  I am interested in methods that work well in practice as well as having nice theoretical properties.</p>

<p>A web search brings up "A sublinear time approximation scheme for clustering in metric spaces" by P. Indyk as the most cited paper in the area.  </p>

CC BY-SA 3.0
2013-03-07T12:49:50.790Z
THere's also lots of work on sampling and streaming-based methods. These tend to work best when the metric is the Euclidean distance (or relatives). I'm not sure what your situation is.
Suresh Venkat
CC BY-SA 3.0
2013-03-07T13:04:11.217Z
@SureshVenkat Weighted Euclidean distance works for me.  I can find lists of papers, what I am not clear on is which are considered the current state of the art from an algorithms/practice point of view.
Majid
<machine-learning><clustering>
Clustering massive data sets in practice
Majid
# Clustering massive data sets in practice

(Asked by: username_0 on 2013-03-07)

If you have a very large data set of $n$ vectors and you want to cluster them according to some metric measure, what is the current state of the art when you can not afford to do more than $\Theta(n)$ work? I am interested in methods that work well in practice as well as having nice theoretical properties.

A web search brings up "A sublinear time approximation scheme for clustering in metric spaces" by P. Indyk as the most cited paper in the area.

**Tags:** machine-learning, clustering

> **Comments:**
> **username_1**: THere's also lots of work on sampling and streaming-based methods. These tend to work best when the metric is the Euclidean distance (or relatives). I'm not sure what your situation is.  
> **username_0**: @SureshVenkat Weighted Euclidean distance works for me.  I can find lists of papers, what I am not clear on is which are considered the current state of the art from an algorithms/practice point of view.  

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## 1 answers

### Answer by username_1 on 2013-03-08. Score: 5

At the risk of sounding immodest, I wrote a [short survey on stream clustering](http://www.cs.utah.edu/~suresh/web/2008/03/29/clustering-on-streams/) a few years ago. It's a little out of date, but not overly so, and doing forward citations will get you to the recent work in the area.

> **Comments:**
> **username_0**: Thank you. I can see that people are very interested in streams. In my problem I have access to all the data, it is just very large.  

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