Clustering Lightning Discharges to Identify Storms

September 13, 2013

(This article was first published on Exegetic Analytics » R, and kindly contributed to R-bloggers)

A short talk that I gave at the LIGHTS 2013 Conference (Johannesburg, 12 September 2013). The slides are relatively devoid of text because I like the audience to hear the content rather than read it. The central message of the presentation is that clustering lightning discharges into storms is not a trivial task, but still a worthwhile challenge because it can lead to some very interesting science!

I evaluated both k-means and hierarchical clustering techniques but stuck with the latter because it was easier to formulate a dissimilarity matrix using great circle (as opposed to Euclidean) distances than to try and force the k-means algorithm to calculate geographic distances. In retrospect, I could have used pam() from the cluster package to do clustering around medoids (and which also uses a dissimilarity matrix). In addition, this would have the advantage of being somewhat more computationally efficient, but experimenting with that will have to wait for another day.

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