Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13053
Title: Discovering association patterns in large spatio-temporal databases
Authors: Lee, EMH
Chan, KCC 
Keywords: Customer profiles
Data mining
Temporal databases
Visual databases
Issue Date: 2006
Publisher: IEEE
Source: Sixth IEEE International Conference on Data Mining Workshops, 2006 : ICDM Workshops 2006, December 2006, Hong Kong, p. 349-354 How to cite?
Abstract: Over the past few years, a considerable number of studies have been made on market basket analysis. Market basket analysis is a useful method for discovering customer purchasing patterns by extracting association from stores' transaction databases. In many business of today, customer transactions can be made in many different geographical locations round the clock, especially after e-business have become prevalent. The traditional methods that consider only the association rules of an individual location or all locations as a whole are not suitable for such a multi-location environment. We design a novel and efficient algorithm for mining spatio-temporal association rules which have multi-level time and location granularities, in spatio-temporal databases. Experimental results have shown that our methods are efficient and we can find spatio-temporal association rules satisfactorily
URI: http://hdl.handle.net/10397/13053
ISBN: 0-7695-2702-7
DOI: 10.1109/ICDMW.2006.62
Appears in Collections:Conference Paper

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