Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/1794
Title: A novel approach for statistical and fuzzy association rule mining on quantitative data
Authors: Krishna, G Vijay
Krishna, P Radha
Keywords: Clustering
Data mining
Fuzzy association rules
Statistical association rules
Issue Date: Jul-2008
Publisher: CSIR
Abstract: This paper presents a method for deriving Association rules by using apriori algorithm, clustering and fuzzy set concepts. Association rules of quantitative data are presented with mean and standard deviation, and with fuzzy linguistic terms. A case study was done on the commodity data to demonstrate vitality of proposed method. The statistical and fuzzy Association rules, inferred from the commodity data set, are helpful for the business experts in exporting related commodities to a set of countries in a more effective way along with high profits.
Description: 512-517
URI: http://hdl.handle.net/123456789/1794
ISSN: 0022-4456
Appears in Collections: JSIR Vol.67(07) [July 2008]

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