Visual Mining of Power Sets with Large Alphabets
Tamara Munzner,
Qiang Kong,
Raymond Ng,
Jordan Lee,
Janek Klawe,
Dragana Radulovic, and
Carson K. Leung
submitted for publication
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Paper
Abstract
We present the PowerSetViewer visualization system for the
lattice-based mining of power sets. Searching for itemsets within the
power set of a universe occurs in many large dataset knowledge
discovery contexts. Using a spatial layout based on a power set
provides a unified 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 power set structure is is
handling large alphabets, because the size of the power set 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.
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Tamara Munzner
Last modified: Wed Dec 25 20:43:12 PST 2013