Logical Itemset Mining
Venue
IEEE International Conference on Data Mining (Workshop) (2012), pp. 603-610
Publication Year
2012
Authors
Shailesh Kumar, Chandrashekhar V., C. V. Jawahar
BibTeX
Abstract
Frequent Itemset Mining (FISM) attempts to find large and frequent itemsets in
bag-of-items data such as retail market baskets. Such data has two properties that
are not naturally addressed by FISM: (i) a market basket might contain items from
more than one customer intent (mixture property) and (ii) only a subset of items
related to a customer intent are present in most market baskets (projection
property). We propose a simple and robust framework called LOGICAL ITEMSET MINING
(LISM) that treats each market basket as a mixture-of, projections-of, latent
customer intents. LISM attempts to discover logical itemsets from such bagof-items
data. Each logical itemset can be interpreted as a latent customer intent in retail
or semantic concept in text tagsets. While the mixture and projection properties
are easy to appreciate in retail domain, they are present in almost all types of
bag-of-items data. Through experiments on two large datasets, we demonstrate the
quality, novelty, and actionability of logical itemsets discovered by the simple,
scalable, and aggressively noise-robust LISM framework. We conclude that while FISM
discovers a large number of noisy, observed, and frequent itemsets, LISM discovers
a small number of high quality, latent logical itemsets.
