Please use this identifier to cite or link to this item:
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.
Page(s): 512-517
ISSN: 0022-4456
Appears in Collections: JSIR Vol.67(07) [July 2008]

Files in This Item:
File Description SizeFormat 
JSIR 67(7) 512-517.pdf105.16 kBAdobe PDFView/Open

Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.