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Title:
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A DATABASE PREPROCESSING APPROACH FOR ASSOCIATION RULE MINING |
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Author(s):
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Chenhan Liao , Frank Wang , Na Helian |
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ISBN:
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978-972-8924-55-3 |
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Editors:
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Piet Kommers and Pedro Isaías |
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Year:
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2008 |
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Edition:
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Single |
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Keywords:
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Database, ARM, Filtering, Apriori-like |
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Type:
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Full Paper |
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First Page:
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243 |
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Last Page:
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252 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Existing Apriori-like association rule mining (ARM) algorithms suffer from high database scanning overhead especially
for high dimensional database. We propose a database preprocessing technique to optimize Apriori-like ARM algorithms
addressing the problem. The classic ARM algorithm Apriori and its variations such as DHP, Partition require multiple
database scans for finding frequent k-itemsets, which is a time consuming procedure. Our method optimizes Apriori
families by utilizing a memory-resident filter to stop unnecessary items in the database transactions from being scanned
during the database iterations. The proposed method can also be applied to reduce data collection cost of ARM in
distributed environments. The performance of the method is validated based on both synthetic datasets and real life
datasets. Moreover, the simulation results of a mobile agent-based filtering design show that this filtering technique can
reduce significant amount of communication cost against various network delays. |
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