Handling missing data in K-Means

Tag: clustering

One of the challenging things related to building "big data" apps is dealing with messy data sets. At SupplyFrame, we ran into a problem while doing some analysis with K-Means clustering:  All interesting features in our data had varying amounts of missing values.  It turns out that how the values are missing is significant!  Say […]