Mining Food Industry’s Multidimensional Data to Produce Association Rules using Apriori Algorithm as a Basis of Business Strategy

ICoICT 2013


Feri Sulianto, The Houw Liong, Arie Ardiyanti

Abstract—The food industry sell a range of product
variations. The company want to take advantage to build
business strategy from huge information which is stored in data
warehouse. In this case, data mining technology needs to be
implemented to explore valuable information on transactional
data to assess customer’s preferences for products sold as a
business strategy.
Information about the way customers buy food products is
necessary, this can be done by mapping the transaction data
which is described as the pattern of consumer’s taste. The
association method using apriori algorithm is used to map
customer’s choice.
The challenge is in the data itself, multidimensional data has
to be prepared first before the data is fetched to the mining
process. Data reduction will be held to handle huge instances
and attributes between the data. Research focus on the way we
handle data until the rules is built. To reach this goal, three
validation levels will be implemented to verify the reliability of
the association rules shows by percentage supports and
Index Terms— Data Reduction, Apriori, Support, Confident,
Association Rules, Three Validation Levels.

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