Comments on: Clustering comes to Flickr

By: rashmi

rashmi — Mon, 22 Aug 2005 03:42:29 +0000

Yes, I like that. I wrote about it here. I think they could improve their algorithm though. I kept on getting this one cluster with about 20 clusters. I increased the number of clusters to try to break that cluster up, but it would drop one or two tags, but mostly remain constant. Good algorithms will give you very different views of the data as you adjust the number of clusters.

It might have been somethign with my data rather than their algorithm though.

But overall, clustering is very promising with tags.

By: Milhouse

Milhouse — Mon, 22 Aug 2005 01:36:33 +0000

A similiar demo app for del.icio.us uses k-clustering for tags. Results shown in tag cloud with more "important" tags in larger text... very nice. see Cluster your del.icio.us tags

By: rashmi

rashmi — Sun, 21 Aug 2005 14:16:36 +0000

karl,

This is fascinating. GIven that Flickr is not doing any type of semantic linking of words that refer to the same language - for example the tag cats is different than cat, so it makes sense that they are not able to link the English word cow with the French word vache.

So the two languages are kept separate each in their own domain, except when a user makes a specific effort to make the linkage, such as a French user using both cow and vache as a tag,

Not showing the Kanji results when you have a photo tagged 牛 is simply inexplicable. I am looking around to see if there are any other Kanji clusters. It would be strange if they do not support clusters in Kanji.

By: karl

karl — Sat, 20 Aug 2005 18:40:46 +0000

Interesting things which illustrate one problem of the tags

In English
http://flickr.com/photos/tags/cow/clusters/

In French
http://flickr.com/photos/tags/vache/clusters/

We can find some "English" tagged cows because French have added both tags cow and vache. Let's try to go a bit deeper.

In Romaji (romanized Japanese)
http://flickr.com/photos/tags/ushi/clusters/

No more cluster, no more association with the cow or vache. Well it might be normal, because it's the romanized version of the kanji for cow.

In Hiragana
http://flickr.com/photos/tags/うし/

Zero results.

In Kanji
http://flickr.com/photos/tags/牛/

Zero results. Japanese people don't have the right to have their images tagged and clustered?
It should have given at least one result
http://flickr.com/photos/karl/1333234/

By: rashmi

rashmi — Fri, 05 Aug 2005 15:42:16 +0000

As regards how the clustering was done - mathematically they probably used something like k-means clustering algorithm. Also, they seem to have specified keeping a small number of clusters - larger number of clusters might have increased the cohesiveness of each cluster.
(Note, I don't know how this was done. This is all speculation on my part.)

By: rashmi

rashmi — Fri, 05 Aug 2005 15:36:19 +0000

True - its hard to tell why the individual tags lie in one group as compared to the other. But a cluster taken as a gestalt does seem to work. So I do get that overall Cluster 3 seems to reflect more of blue sky in summer, while Cluster 4 is more about the flowers, gardens. The difference is subtle but it seems to be work for me - especially when I go to look at a pictures for Cluster 3 and 4.

I am not sure such clusters would help people predict where they might find a particular picture (it might lie in one or the other cluster). Very hard to predict. But it works well for exploration - for getting a sense of Cluster 1 as compared to Cluster 4.

As such Flickr's clusters serve a different purpose than say, Google Image search: its more about exploration than predictably find a particular picture.

By: raina

raina — Fri, 05 Aug 2005 14:29:13 +0000

I have been researhing on Folksonomies.What I have not been able to figure out is on what basis is this clustering done? I mean, there seems to be no particular theme dominant in one cluster('park' and 'tree' lies in 1 cluster while 'garden' and 'grass' lies in another)
I personally found the clusters very confusing as there was no semantic closeness in the tags within a cluster, which I think would have been a more appropriate approach to clustering...if the user is not able to see the pattern in the cluster....it defeats the whole purpose of clustering, dont u think?

By: abc

abc — Fri, 05 Aug 2005 13:53:53 +0000

I agree!
Not naming the clusters was a good move. Staying away from slick visualizations is almost ALWAYS a good move (for some reason, visualizations are generally not as effective as designers think they are going to be : not sure what the reason for this is).