Cyclic association rules
Ozden, B.
Ramaswamy, S.
Silberschatz, A.
Inf. Sci. Res. Centre, Bell Labs., Murray Hill, NJ ;
This paper appears in: Data Engineering, 1998. Proceedings., 14th International Conference on
Publication Date: 23-27 Feb 1998
On page(s): 412-421
Meeting Date: 02/23/1998 - 02/27/1998
Location: Orlando, FL, USA
ISSN: 1063-6382
ISBN: 0-8186-8289-2
References Cited: 10
INSPEC Accession Number: 5850658
Digital Object Identifier: 10.1109/ICDE.1998.655804
Current Version Published: 2002-08-06
Abstract
We study the problem of discovering association rules that display
regular cyclic variation over time. For example, if we compute
association rules over monthly sales data, we may observe seasonal
variation where certain rules are true at approximately the same month
each year. Similarly, association rules can also display regular hourly,
daily, weekly, etc., variation that is cyclical in nature. We
demonstrate that existing methods cannot be naively extended to solve
this problem of cyclic association rules. We then present two new
algorithms for discovering such rules. The first one, which we call the
sequential algorithm, treats association rules and cycles more or less
independently. By studying the interaction between association rules and
time, we devise a new technique called cycle pruning, which reduces the
amount of time needed to find cyclic association rules. The second
algorithm, which we call the interleaved algorithm, uses cycle pruning
and other optimization techniques for discovering cyclic association
rules. We demonstrate the effectiveness of the interleaved algorithm
through a series of experiments. These experiments show that the
interleaved algorithm can yield significant performance benefits when
compared to the sequential algorithm. Performance improvements range
from 5% to several hundred percent
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