Weighted Association Rule Mining using Weighted Support and Significance Framework
Weighted Association Rule Mining using Weighted Support and Significance Framework
We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to have a weight. The goal is to steer the mining focus to those significant relationships involving items with significant weights rather than being flooded in the combinatorial explosion of insignificant relationships. We identify the challenge of using weights in the iterative process of generating large itemsets. The problem of invalidation of the “downward closure property” in the weighted setting is solved by using an improved model of weighted support measurements and exploiting a “weighted downward closure property”. A new algorithm called WARM (Weighted Association Rule Mining) is developed based on the improved model. The algorithm is both scalable and efficient in discovering significant relationships in weighted settings as illustrated by experiments performed on simulated datasets.
Weighted Association Rule Mining, Weighted Support, Significant relationship, weighted downward closure roperty, WARM algorithm
661-666
Tao, Feng
3d9fc416-da70-4ee2-87c4-6ba0a1d26461
Murtagh, Fionn
b1a5f04b-d373-4403-9d29-73273f1e6ce9
Farid, Mohsen
2322307a-2cb9-44a0-b3cd-1edb966dfe4d
2003
Tao, Feng
3d9fc416-da70-4ee2-87c4-6ba0a1d26461
Murtagh, Fionn
b1a5f04b-d373-4403-9d29-73273f1e6ce9
Farid, Mohsen
2322307a-2cb9-44a0-b3cd-1edb966dfe4d
Tao, Feng, Murtagh, Fionn and Farid, Mohsen
(2003)
Weighted Association Rule Mining using Weighted Support and Significance Framework.
The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD 2003), Washington DC, United States.
24 - 27 Aug 2003.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to have a weight. The goal is to steer the mining focus to those significant relationships involving items with significant weights rather than being flooded in the combinatorial explosion of insignificant relationships. We identify the challenge of using weights in the iterative process of generating large itemsets. The problem of invalidation of the “downward closure property” in the weighted setting is solved by using an improved model of weighted support measurements and exploiting a “weighted downward closure property”. A new algorithm called WARM (Weighted Association Rule Mining) is developed based on the improved model. The algorithm is both scalable and efficient in discovering significant relationships in weighted settings as illustrated by experiments performed on simulated datasets.
More information
Published date: 2003
Additional Information:
Event Dates: August 24 - 27
Venue - Dates:
The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD 2003), Washington DC, United States, 2003-08-24 - 2003-08-27
Keywords:
Weighted Association Rule Mining, Weighted Support, Significant relationship, weighted downward closure roperty, WARM algorithm
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 257986
URI: http://eprints.soton.ac.uk/id/eprint/257986
PURE UUID: a0126934-3882-4ab9-80e3-5e619ab1c3d6
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Date deposited: 24 Jul 2003
Last modified: 14 Mar 2024 06:04
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Contributors
Author:
Feng Tao
Author:
Fionn Murtagh
Author:
Mohsen Farid
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