Fast Rule Mining over Multi-dimensional Windows

Mahasweta Das, Deepak Padmanabhan, Prasad Deshpande, Ramakrishnan Kannan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Association rule mining is an indispensable tool for discovering
insights from large databases and data warehouses.
The data in a warehouse being multi-dimensional, it is often
useful to mine rules over subsets of data defined by selections
over the dimensions. Such interactive rule mining
over multi-dimensional query windows is difficult since rule
mining is computationally expensive. Current methods using
pre-computation of frequent itemsets require counting
of some itemsets by revisiting the transaction database at
query time, which is very expensive. We develop a method
(RMW) that identifies the minimal set of itemsets to compute
and store for each cell, so that rule mining over any
query window may be performed without going back to the
transaction database. We give formal proofs that the set of
itemsets chosen by RMW is sufficient to answer any query
and also prove that it is the optimal set to be computed
for 1 dimensional queries. We demonstrate through an extensive
empirical evaluation that RMW achieves extremely
fast query response time compared to existing methods, with
only moderate overhead in pre-computation and storage
Original languageEnglish
Title of host publicationProceedings of the Eleventh SIAM International Conference on Data Mining, SDM 2011
Number of pages12
Publication statusPublished - 2011
EventSDM 2011 - Arizona, Phoenix, United States
Duration: 28 Apr 201130 Apr 2011


ConferenceSDM 2011
Country/TerritoryUnited States


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