This is a fun image I found on Neil Kodner’s blog: But I’ve never actually watched any of the Star Trek movies, so I decided to recreate the graph with Pikachu instead: Here’s a smoothed version to better compare the counts … Continue reading →

I describe a kernel density approach to outlier detection on small datasets. In particular, my model is the set of prices for a given item that can be found online. Introduction Suppose you’re searching online for the cheapest place to … Continue reading →

Aaron Koblin’s Sheep Market visualization is an awesome use of Mechanical Turk. But it’d be even more awesome if the grid were ordered, so inspired by the use of eigenfaces in facial recognition, I decided to try projecting the sheep … Continue reading →

Given a set of numerical datapoints, we often want to know how many clusters the datapoints form. Two practical algorithms for determining the number of clusters are the gap statistic and the prediction strength. Gap Statistic The gap statistic algorithm … Continue reading →