What does color mean here? What about width of the box?
When you say median or mean of all cluster distances, do you mean across that single run? I think that this plot is fine as it is except that it needs a legend that explains all of these issues. My general rule of thumb is that most figures should have what I call a "Kipling caption". See the caption of the first image here: http://www.boop.org/jan/justso/butter.htm to see what I mean by this. Imagine that there is a very mathematically inclined 4 year old who is looking at your diagram and quizzing you about every part. Answer all their questions in the caption and you have a Kipling caption. For comparing different runs of the clustering or different algorithms, I think that a cumulative distribution plot (using plot.ecdf) with all of the different algorithms on one plot would be the best comparison tool. On Fri, Feb 22, 2013 at 8:33 AM, Dan Filimon <dangeorge.fili...@gmail.com>wrote: > As most of the regulars know, I'm working with Ted Dunning on a new > clustering framework for Mahout that should land in 0.8. > > Part of my work is comparing the clustering quality of the new code > with the existing Mahout implementation. > > I compiled a CSV of the quality data [1]. I ran 5 runs of the > clustering on the 20 newsgroups data set comparing Mahout KMeans (km), > Ball KMeans (bkm), Streaming KMeans (skm) and Streaming KMeans > followed by Ball KMeans (bskm). > > I'm looking at now making some appealing plots for the data. For > instance, I think want to make box plots of individual clustering > runs. Here's an example [2] of what a clustering looks like for one > run of Mahout's standard k-means. > > There's a box for each cluster, the mean distance is the thick line, > the limits are the 1st and 3rd quartiles and the whiskers are the min > and max distances. > The blue horizontal line is the mean of all average cluster distances. > The green horizontal line is the median of all average cluster distances. > > I intend on making similar plots for the other runs and then > aggregating the means of the runs into box plots for the different > classes of k-means. > The main result being that streaming k-means + ball k-means (as done > in the MR) gives a high quality clustering. > > How do you feel about this plot? Is it too dense? Too colorful? Should > I not draw the median any more? > What are some other good ways of plotting the quality given the data set? > > Thanks! > > [1] > https://github.com/dfilimon/mahout/blob/skm/examples/src/main/resources/kmeans-comparison-nospace.csv > [2] > http://swarm.cs.pub.ro/~dfilimon/skm-mahout/Mahout%20KMeans%20Run%201.pdf >
