Geometrically inspired itemset mining

Florian Verhein, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations


In our geometric view, an itemset is a vector (Itemvector) in the space of transactions. Linear and potentially non-linear transformations can be applied to the itemvectors before mining patterns. Aggregation functions and interestingness measures can be applied to the transformed vectors and pushed inside the mining process. We show that interesting itemset mining can be carried out by instantiating four abstract functions: a transformation (g), an algebraic aggregation operator (o) and measures (f and F). For Frequent Itemset Mining (FIM), g and F are identity transformations, o is intersection and f is the cardinality. Based on this geometric view we present a novel algorithm that uses space linear in the number of 1-itemsets to mine all interesting itemsets in a single pass over the data, with no candidate generation. It scales (roughly) linearly in running time with the number of interesting itemsets. FIM experiments show that it outperforms FPGrowth on realistic datasets above a small support threshold (0.29% and 1.2% in our experiments)1.

Original languageEnglish
Title of host publicationProceedings - Sixth International Conference on Data Mining, ICDM 2006
Number of pages12
StatePublished - 2006
Externally publishedYes
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: Dec 18 2006Dec 22 2006

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference6th International Conference on Data Mining, ICDM 2006
CityHong Kong


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