Visual Mining of Powersets with Large Alphabets

Masters Thesis, May 2006

by: Qiang Kong
supervisor: Tamara Munzner and Raymond T. Ng
supervisory committee member: Michiel van de Panne


We present the PowerSetViewer visualization system for the lattice-based mining of powersets. Searching for items within the powerset of a universe occurs in many large dataset knowledge discovery contexts. Using a spatial layout based on a powerset provides a united visual framework at three different levels: data mining on the filtered dataset, browsing the entire dataset, and comparing multiple datasets sharing the same alphabet. The features of our system allow users to find appropriate parameter settings for data mining algorithms through lightweight visual experimentation showing partial results. We use dynamic constrained frequent set mining as a concrete case study to showcase the utility of the system. The key challenge for spatial layouts based on powerset structure is handling large alphabets, because the size of the powerset grows exponentially with the size of the alphabet. We present scalable algorithms for enumerating and displaying datasets containing between 1.5 and 7 million itemsets, and alphabet sizes of over 40,000.