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Park, Hee-Chang;Cho, Kwang-Hyun
- 한국데이터정보과학회:학술대회논문집
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- 2004.10a
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- pp.115-124
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- 2004
Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.
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황준현;김재련
- Proceedings of the Korea Inteligent Information System Society Conference
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- 2002.11a
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- pp.241-249
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- 2002
Making traditional plan of target marketing based on Association Rule has brought restriction to obtain the target of marketing. This paper is to present Inverse Association Rule as a new association rule for target marketing. Inverse Association Rule does not use information about relation between items that customers purchase like Association Rule, but use information about relation between items that customers do not pruchase. By adding Inverse Association Rule to target marketing, we generate new marketing rule to look for new target of marketing. From new marketing rule, this paper is to show direct marketing about target item and indirect marketing about another item associated with target item to sell target item. The reason is that sales of the item associated with target item have an influence on sales of target item.
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