DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Revealing complex associations between entities is of vast significance for business optimization, prediction and decision making. Such associations include not only co-occurrence-based explicit relations but also non co-occurrence-based implicit ones. Associative rule mining (ARM) is used to study these implicit and explicit relationships. Online shopping customer review (OSCR) data has become a major information resource for consumers and has extremely important implications for a wide range of management activities. Consumer reviews examine the bond between service quality and customer purchase behaviour in online shopping context. Apriori is a key algorithm for mining frequent item sets for Boolean association rules. To develop the efficiency of the level-wise generation of frequent itemsets in online customer shopping customer review data, Apriori property is used to reduce the search space .The detection of interesting patterns in this collection of data can guide to important marketing and management strategic decisions. In this survey paper, some of the research work carried out on customer online shopping data is discussed. Also, the use of Apriori algorithm for the same type of data set is analyzed.
